From 9651642aec1f1daa6cfa5649aca65b688d0631cc Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Tue, 9 Apr 2024 10:33:57 +0000
Subject: [PATCH] deploy: ba55bee0b1a7bf2f703f5d8a5fc5d9f4f5655287

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Introduction",id:"1-introduction",level:2},{value:"Why Sort?",id:"why-sort",level:3},{value:"Code Quest Begins",id:"code-quest-begins",level:3},{value:"2. Bubble Sort",id:"2-bubble-sort",level:2},{value:"Pythonic Bubbles",id:"pythonic-bubbles",level:3},{value:"3. Merge Sort",id:"3-merge-sort",level:2},{value:"Pythonic Tea Ceremony",id:"pythonic-tea-ceremony",level:3},{value:"4. Insertion Sort",id:"4-insertion-sort",level:2},{value:"Pythonic Card Tricks",id:"pythonic-card-tricks",level:3},{value:"5. Quick Sort",id:"5-quick-sort",level:2},{value:"Pythonic Dance Moves",id:"pythonic-dance-moves",level:3},{value:"6. Heap Sort",id:"6-heap-sort",level:2},{value:"Pythonic Library Stacks",id:"pythonic-library-stacks",level:3},{value:"7. Tim Sort",id:"7-tim-sort",level:2},{value:"Pythonic Incantations",id:"pythonic-incantations",level:3},{value:"8. Radix Sort",id:"8-radix-sort",level:2},{value:"Pythonic Incantations",id:"pythonic-incantations-1",level:3},{value:"9. When to Use Which Sorting Algorithm",id:"9-when-to-use-which-sorting-algorithm",level:2},{value:"The Sorting Compass",id:"the-sorting-compass",level:3},{value:"Decision-Making Elixirs",id:"decision-making-elixirs",level:3},{value:"Parting Spell",id:"parting-spell",level:3}];function h(e){const n={admonition:"admonition",code:"code",h1:"h1",h2:"h2",h3:"h3",li:"li",ol:"ol",p:"p",pre:"pre",strong:"strong",ul:"ul",...(0,r.a)(),...e.components};return(0,i.jsxs)(i.Fragment,{children:[(0,i.jsx)(n.h1,{id:"a-guide-to-sorting",children:"A Guide to Sorting"}),"\n",(0,i.jsx)(n.h2,{id:"1-introduction",children:"1. Introduction"}),"\n",(0,i.jsx)(n.p,{children:"Sorting is like tidying up your room after a wild party. But instead of picking up empty cups and crumpled napkins, we're wrangling data. Imagine a chaotic array of numbers, letters, or even mystical runes (hey, we don't judge). Our mission? To transform this unruly mess into a neat, orderly sequence."}),"\n",(0,i.jsx)(n.h3,{id:"why-sort",children:"Why Sort?"}),"\n",(0,i.jsx)(n.p,{children:"Before we dive into the nitty-gritty, let's ponder the cosmic significance of sorting. Why bother? Well, my fellow data voyagers, sorting isn't just about aesthetics. It's about efficiency, searchability, and making sense of the universe (okay, maybe not the last part)."}),"\n",(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Searchability:"}),' Imagine a phone book (remember those?). Without sorting, finding "Zachary Zebra" would involve flipping through pages like a caffeinated squirrel. Sorted? Bam!']}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Efficiency:"})," Algorithms crave efficiency like a cat craves sunbeams. Sorting helps us optimize search, merge datasets, and prepare data for other operations. Efficiency is our golden ticket to algorithmic stardom."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"code-quest-begins",children:"Code Quest Begins"}),"\n",(0,i.jsx)(n.p,{children:"In the upcoming sections, we'll delve into sorting algorithms. We'll write Python code and explore their strengths, weaknesses, and secret handshakes. Brace yourselves for practical tips, eureka moments, and maybe a meme or two."}),"\n",(0,i.jsx)(n.p,{children:"So grab your favorite caffeinated beverage (mine's a double-shot espresso), open your Python IDE, and let's embark on this sorting odyssey. Remember, sorting isn't just about order\u2014it's about finding beauty in chaos. \ud83c\udf1f\ud83d\udd0d\ud83d\udc0d"}),"\n",(0,i.jsx)(n.h2,{id:"2-bubble-sort",children:"2. Bubble Sort"}),"\n",(0,i.jsx)(n.p,{children:"Bubble Sort's dance moves are simple:"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Bubble Up:"})," Imagine bubbles rising in a fizzy soda. Similarly, Bubble Sort compares adjacent elements in your list. If they're out of order, it swaps them."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Repeat:"}),' Keep bubbling up the list until everything settles into place. It\'s like a never-ending game of "Is this in the right order?"']}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"pythonic-bubbles",children:"Pythonic Bubbles"}),"\n",(0,i.jsx)(n.p,{children:"Let's brew some Python tea. Here's our Bubble Sort recipe:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:'title="Python"',children:'def bubble_sort(arr):\n    n = len(arr)\n    for i in range(n):\n        # Last i elements are already sorted\n        for j in range(0, n - i - 1):\n            if arr[j] > arr[j + 1]:\n                # Swap adjacent elements\n                arr[j], arr[j + 1] = arr[j + 1], arr[j]\n\n\n# Example usage\nmy_list = [5, 2, 9, 1, 5, 6]\nbubble_sort(my_list)\nprint("Sorted list:", my_list)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{children:"Sorted list: [1, 2, 5, 5, 6, 9]\n"})}),"\n",(0,i.jsx)(n.admonition,{type:"tip",children:(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Bubble Sort isn't the fastest kid on the block. It's like the tortoise in the sorting race. But hey, it gets the job done eventually."}),"\n",(0,i.jsx)(n.li,{children:"If your list is mostly sorted already, Bubble Sort performs better. It's like tidying up a room that's already fairly neat."}),"\n"]})}),"\n",(0,i.jsx)(n.h2,{id:"3-merge-sort",children:"3. Merge Sort"}),"\n",(0,i.jsxs)(n.p,{children:["Merge Sort follows a simple yet profound philosophy: ",(0,i.jsx)(n.strong,{children:"divide and conquer"}),". It's like splitting a giant puzzle into smaller, solvable pieces. Here's the playbook:"]}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Divide:"})," Take your unsorted list and split it into halves. Keep doing this until you have tiny, sorted sublists. Think of it as a peaceful tea ceremony\u2014each cup represents a sublist."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Conquer:"})," Now, merge those sublists back together. But not haphazardly! Merge them in a way that maintains order. Imagine arranging tea cups by size\u2014small to large."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Repeat:"})," Keep dividing, conquering, and merging until your entire list is sorted. It's like assembling a thousand origami cranes\u2014one fold at a time."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"pythonic-tea-ceremony",children:"Pythonic Tea Ceremony"}),"\n",(0,i.jsx)(n.p,{children:"Let's brew some Python code, shall we? Our teapot (read: Python IDE) awaits:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:'title="Python"',children:'def merge_sort(arr):\n    if len(arr) <= 1:\n        return arr  # Already sorted or empty\n\n    # Divide: Split the list into halves\n    mid = len(arr) // 2\n    left_half = arr[:mid]\n    right_half = arr[mid:]\n\n    # Recursively sort each half\n    left_sorted = merge_sort(left_half)\n    right_sorted = merge_sort(right_half)\n\n    # Conquer: Merge the sorted halves\n    return merge(left_sorted, right_sorted)\n\n\ndef merge(left, right):\n    result = []\n    left_idx, right_idx = 0, 0\n\n    while left_idx < len(left) and right_idx < len(right):\n        if left[left_idx] < right[right_idx]:\n            result.append(left[left_idx])\n            left_idx += 1\n        else:\n            result.append(right[right_idx])\n            right_idx += 1\n\n    # Handle remaining elements (if any)\n    result.extend(left[left_idx:])\n    result.extend(right[right_idx:])\n    return result\n\n\n# Example usage\nmy_list = [5, 2, 9, 1, 5, 6]\nsorted_list = merge_sort(my_list)\nprint("Sorted list:", sorted_list)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{children:"Sorted list: [1, 2, 5, 5, 6, 9]\n"})}),"\n",(0,i.jsx)(n.admonition,{type:"tip",children:(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Merge Sort is like a calm river flowing through your data. It's stable, predictable, and won't surprise you with sudden rapids."}),"\n",(0,i.jsx)(n.li,{children:"Its time complexity? A soothing O(n log n). Perfect for contemplative evenings."}),"\n"]})}),"\n",(0,i.jsx)(n.h2,{id:"4-insertion-sort",children:"4. Insertion Sort"}),"\n",(0,i.jsx)(n.p,{children:"Imagine you're holding a deck of playing cards. Your goal? Arrange them in ascending order. Here's how Insertion Sort plays the game:"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Virtual Split:"})," The array is virtually split into two parts: the ",(0,i.jsx)(n.strong,{children:"sorted"})," and the ",(0,i.jsx)(n.strong,{children:"unsorted"}),". The sorted part starts empty, like an empty card table."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Pick and Place:"})," From the unsorted part, pick a card (element) and place it in its correct position within the sorted part. It's like adding a card to your sorted hand."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Repeat:"})," Keep picking cards from the unsorted part and inserting them into the sorted part. Each insertion expands the sorted section."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"pythonic-card-tricks",children:"Pythonic Card Tricks"}),"\n",(0,i.jsx)(n.p,{children:"Let's shuffle our Python deck and perform some card magic (no top hats required):"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:'title="Python"',children:'def insertion_sort(arr):\n    for i in range(1, len(arr)):\n        key = arr[i]  # The card we\'re inserting\n        j = i - 1\n\n        # Compare with cards in the sorted part\n        while j >= 0 and arr[j] > key:\n            arr[j + 1] = arr[j]  # Shift larger cards to the right\n            j -= 1\n\n        arr[j + 1] = key  # Insert the card in its correct position\n\n\n# Example usage\nmy_cards = [5, 2, 9, 1, 5, 6]\ninsertion_sort(my_cards)\nprint("Sorted cards:", my_cards)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{children:"Sorted cards: [1, 2, 5, 5, 6, 9]\n"})}),"\n",(0,i.jsx)(n.admonition,{type:"tip",children:(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Insertion Sort is like arranging cards in your hand. It's simple, stable, and patient."}),"\n",(0,i.jsx)(n.li,{children:"Use it when your list is small or nearly sorted. It's not the fastest, but it's reliable."}),"\n"]})}),"\n",(0,i.jsx)(n.h2,{id:"5-quick-sort",children:"5. Quick Sort"}),"\n",(0,i.jsxs)(n.p,{children:["Quick Sort's secret sauce? ",(0,i.jsx)(n.strong,{children:"Pivoting"}),". Imagine a dance floor where elements groove to the beat. Here's how it works:"]}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Choose a Pivot:"})," Our DJ (that's you) picks an element from the list. This chosen one is the pivot\u2014the life of the party."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Partitioning:"})," Now, let's create two groups: those who dance better than the pivot and those who need a few lessons. Elements greater than the pivot join the cool kids' club, while the rest form their own clique."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Repeat:"})," Recursively apply Quick Sort to both groups. It's like splitting the dance floor into smaller circles, each with its own pivot."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"pythonic-dance-moves",children:"Pythonic Dance Moves"}),"\n",(0,i.jsx)(n.p,{children:"Grab your Python dancing shoes (they're neon green, trust me) and let's write some code:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:'title="Python"',children:'def quick_sort(arr):\n    if len(arr) <= 1:\n        return arr  # Already sorted or empty\n\n    pivot = arr[len(arr) // 2]  # Choose a pivot\n    left, equal, right = [], [], []\n\n    for element in arr:\n        if element < pivot:\n            left.append(element)\n        elif element == pivot:\n            equal.append(element)\n        else:\n            right.append(element)\n\n    # Recursively sort the partitions\n    return quick_sort(left) + equal + quick_sort(right)\n\n\n# Example usage\nmy_list = [8, 3, 1, 6, 4, 7]\nsorted_list = quick_sort(my_list)\nprint("Sorted list:", sorted_list)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{children:"Sorted list: [1, 3, 4, 6, 7, 8]\n"})}),"\n",(0,i.jsx)(n.admonition,{type:"tip",children:(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Quick Sort is like a DJ remixing your playlist. It's fast, unpredictable, and occasionally drops the bass (or swaps elements)."}),"\n",(0,i.jsx)(n.li,{children:"Its average-case time complexity? A lightning-quick O(n log n). Perfect for impromptu dance-offs."}),"\n"]})}),"\n",(0,i.jsx)(n.h2,{id:"6-heap-sort",children:"6. Heap Sort"}),"\n",(0,i.jsxs)(n.p,{children:["Imagine a library with neatly stacked books. Each book has a priority (think Dewey Decimal System). Heap Sort builds a ",(0,i.jsx)(n.strong,{children:"binary heap"}),", which is like a well-behaved tree. Here's the hushed process:"]}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Heapify:"}),' Arrange your unsorted list into a binary heap. It\'s like whispering to the books, "Find your proper place, my dear."']}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Sorting Act:"})," Extract the root (highest priority) element and replace it with the last leaf. Then, restore the heap property. Repeat until the entire heap is sorted. It's like a silent ballet\u2014graceful and efficient."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"pythonic-library-stacks",children:"Pythonic Library Stacks"}),"\n",(0,i.jsx)(n.p,{children:"Let's tiptoe through the code stacks (no shushing required):"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:'title="Python"',children:'def heapify(arr, n, root):\n    largest = root\n    left_child = 2 * root + 1\n    right_child = 2 * root + 2\n\n    if left_child < n and arr[left_child] > arr[largest]:\n        largest = left_child\n\n    if right_child < n and arr[right_child] > arr[largest]:\n        largest = right_child\n\n    if largest != root:\n        arr[root], arr[largest] = arr[largest], arr[root]\n        heapify(arr, n, largest)\n\n\ndef heap_sort(arr):\n    n = len(arr)\n\n    # Build a max heap\n    for i in range(n // 2 - 1, -1, -1):\n        heapify(arr, n, i)\n\n    # Extract elements one by one\n    for i in range(n - 1, 0, -1):\n        arr[i], arr[0] = arr[0], arr[i]  # Swap root with last element\n        heapify(arr, i, 0)\n\n\n# Example usage\nmy_list = [12, 11, 13, 5, 6, 7]\nheap_sort(my_list)\nprint("Sorted list:", my_list)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{children:"Sorted list: [5, 6, 7, 11, 12, 13]\n"})}),"\n",(0,i.jsx)(n.admonition,{type:"tip",children:(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Heap Sort is like a librarian who alphabetizes books without making a sound. Its time complexity? A hushed O(n log n)."}),"\n",(0,i.jsx)(n.li,{children:"Use it when you need a sorted list but don't want to disturb the other algorithms at the party."}),"\n"]})}),"\n",(0,i.jsx)(n.h2,{id:"7-tim-sort",children:"7. Tim Sort"}),"\n",(0,i.jsx)(n.p,{children:"Tim Sort isn't your average sorting hero. It's a hybrid, a fusion of ancient wisdom and modern pragmatism. Picture this:"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Merge Sort Meets Insertion Sort:"})," Tim Sort marries the elegance of Merge Sort with the practicality of Insertion Sort. It's like inviting Gandalf and Hermione to the same tea party."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Gallop, Gallop, Gallop:"})," Tim Sort gallops through your list, sniffing out order like a seasoned detective. It's adaptive\u2014gentle with nearly sorted data, fierce when chaos reigns."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"The Power of Runs:"})," Tim Sort breaks your list into ",(0,i.jsx)(n.strong,{children:"runs"}),"\u2014chunks of sorted or semi-sorted elements. Then it merges these runs, creating harmony in the chaos."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"pythonic-incantations",children:"Pythonic Incantations"}),"\n",(0,i.jsx)(n.p,{children:"Let's weave some Python spells (no wands required, just code):"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:'title="Python"',children:'def tim_sort(arr):\n    min_run = 32  # Minimum size of a run\n    n = len(arr)\n\n    # Create runs\n    for start in range(0, n, min_run):\n        end = min(start + min_run, n)\n        insertion_sort(arr, start, end)\n\n    # Merge runs\n    size = min_run\n    while size < n:\n        for start in range(0, n, 2 * size):\n            mid = min(start + size, n)\n            end = min(start + 2 * size, n)\n            merge(arr, start, mid, end)\n        size *= 2\n\n\ndef insertion_sort(arr, start, end):\n    # Insertion Sort within a run\n    for i in range(start + 1, end):\n        key = arr[i]\n        j = i - 1\n        while j >= start and arr[j] > key:\n            arr[j + 1] = arr[j]\n            j -= 1\n        arr[j + 1] = key\n\n\ndef merge(arr, start, mid, end):\n    # Merge two runs\n    left, right = arr[start:mid], arr[mid:end]\n    i, j, k = 0, 0, start\n\n    while i < len(left) and j < len(right):\n        if left[i] <= right[j]:\n            arr[k] = left[i]\n            i += 1\n        else:\n            arr[k] = right[j]\n            j += 1\n        k += 1\n\n    arr[k:end] = left[i:] + right[j:]\n\n\n# Example usage\nmy_list = [10, 7, 3, 8, 5, 1]\ntim_sort(my_list)\nprint("Sorted list:", my_list)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{children:"Sorted list: [1, 3, 5, 7, 8, 10]\n"})}),"\n",(0,i.jsx)(n.admonition,{type:"tip",children:(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Tim Sort is like a seasoned traveler who adapts to any terrain. Its time complexity? A mystical O(n log n)."}),"\n",(0,i.jsx)(n.li,{children:"Use it when you need stability and efficiency."}),"\n"]})}),"\n",(0,i.jsx)(n.h2,{id:"8-radix-sort",children:"8. Radix Sort"}),"\n",(0,i.jsxs)(n.p,{children:["Radix Sort doesn't play by the usual rules. It's not concerned with comparisons or pivots. Instead, it focuses on the ",(0,i.jsx)(n.strong,{children:"magical digits"})," within numbers. Here's the incantation:"]}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Least Significant Digit (LSD) Variant:"})," Imagine sorting playing cards by their suit. Radix Sort starts with the rightmost digit (the least significant) and arranges the cards into buckets (one for each digit). Then it repeats the process for the tens place, hundreds place, and so on. Like a patient librarian organizing a chaotic bookshelf."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Most Significant Digit (MSD) Variant:"})," This time, Radix Sort begins with the leftmost digit (the most significant). It's like sorting books by their genre\u2014first all the mysteries, then the romances, and so forth. The process continues recursively until the entire list is sorted."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"pythonic-incantations-1",children:"Pythonic Incantations"}),"\n",(0,i.jsx)(n.p,{children:"Let's summon our Python spellbook and write some code runes:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:'title="Python"',children:'def radix_sort(arr):\n    max_digit_count = len(str(max(arr)))  # Maximum number of digits\n\n    for digit_position in range(max_digit_count):\n        buckets = [[] for _ in range(10)]  # Create 10 magical buckets\n\n        for num in arr:\n            digit = (num // 10**digit_position) % 10\n            buckets[digit].append(num)\n\n        arr = [num for bucket in buckets for num in bucket]\n\n    return arr\n\n\n# Example usage\nmy_numbers = [123, 45, 789, 12, 567, 890]\nsorted_numbers = radix_sort(my_numbers)\nprint("Sorted numbers:", sorted_numbers)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{children:"Sorted numbers: [12, 45, 123, 567, 789, 890]\n"})}),"\n",(0,i.jsx)(n.admonition,{type:"tip",children:(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Radix Sort is like a numerologist arranging cosmic energies. Its time complexity? A mystical O(nk), where k is the number of digits."}),"\n",(0,i.jsx)(n.li,{children:"Use it when you want to sort numbers without comparing their souls (I mean values)."}),"\n"]})}),"\n",(0,i.jsx)(n.h2,{id:"9-when-to-use-which-sorting-algorithm",children:"9. When to Use Which Sorting Algorithm"}),"\n",(0,i.jsx)(n.p,{children:"Dear data voyagers, let's don our sorting hats and discuss the art of algorithmic decision-making. Choosing the right sorting spell for the job is like selecting the perfect wand\u2014essential for a magical outcome. \ud83c\udf1f\ud83d\udd2e"}),"\n",(0,i.jsx)(n.h3,{id:"the-sorting-compass",children:"The Sorting Compass"}),"\n",(0,i.jsx)(n.p,{children:"Before we dive into specifics, let's consult our sorting compass:"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Bubble Sort:"})," Use it when your list is small or nearly sorted. It's like tidying up a room that's already fairly neat. But beware\u2014it's not the fastest broomstick in the sky."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Merge Sort:"})," When stability and predictability matter, Merge Sort is your go-to."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Insertion Sort:"})," It's like arranging cards in your hand. Use it when your list is small or nearly sorted. It's not the fastest, but it's reliable."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Quick Sort:"})," When you need raw speed, summon Quick Sort. Just keep an eye on its worst-case mood swings\u2014it can trip over its own shoelaces."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Heap Sort:"})," Heap Sort builds a binary heap, using space efficiently. Use it when you want reliability without fanfare."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Tim Sort:"})," Tim Sort combines Merge Sort and Insertion Sort. It's adaptive and stable. When in doubt, trust Tim\u2014it's like having a Gandalf-Hermione hybrid on your team."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Radix Sort:"})," Radix Sort sorts numbers by their digits. Use it when you want to sort without comparing values."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"decision-making-elixirs",children:"Decision-Making Elixirs"}),"\n",(0,i.jsx)(n.p,{children:"Here are some practical guidelines:"}),"\n",(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Small Lists:"})," For tiny lists, Bubble Sort is the friendly neighbor who pops over for a cup of sugar. It's simple, but don't invite it to a royal ball (large lists)."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Nearly Sorted Data:"})," Merge Sort is the calm river flowing through your data garden. It's stable and predictable. Use it when your data is already in a zen-like state."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Speed Demons:"})," Quick Sort is the adrenaline junkie. When you need raw speed, Quick Sort pivots and dances faster than a caffeinated squirrel. Just keep an eye on its worst-case mood swings."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Binary Heap Magic:"}),' Heap Sort is the minimalist librarian. It builds a binary heap, using space efficiently. When memory is scarce, Heap Sort whispers, "Fear not, I won\'t clutter your RAM."']}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Pythonic Charm:"})," Tim Sort is Python's own. It adapts, blends, and knows the best pizza joints. When in doubt, trust Tim\u2014it's seen more magic than Merlin."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Digit Sorcery:"})," Radix Sort sorts by digits. It's like numerology for numbers. Use it when you want to sort without comparing values."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Card Arrangement:"})," Insertion Sort is the gentle card arranger. Use it when your list is small or nearly sorted. It's not the fastest, but it's reliable."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"parting-spell",children:"Parting Spell"}),"\n",(0,i.jsx)(n.p,{children:"So, fellow enchanters, choose wisely. Whether you're organizing a potion cabinet or sorting a massive dataset, remember: each algorithm has its own wand, and the right one can turn chaos into enchantment. \ud83c\udf1f\ud83d\udd2e\ud83d\udcdc"}),"\n",(0,i.jsx)(n.p,{children:"Now, go forth and sort\u2014may your lists be ordered!"})]})}function c(e={}){const{wrapper:n}={...(0,r.a)(),...e.components};return n?(0,i.jsx)(n,{...e,children:(0,i.jsx)(h,{...e})}):h(e)}},11151:(e,n,t)=>{t.d(n,{Z:()=>a,a:()=>o});var i=t(67294);const r={},s=i.createContext(r);function o(e){const n=i.useContext(s);return i.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function a(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(r):e.components||r:o(e.components),i.createElement(s.Provider,{value:n},e.children)}}}]);
\ No newline at end of file
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-"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[9274],{53473:(e,n,t)=>{t.r(n),t.d(n,{assets:()=>l,contentTitle:()=>o,default:()=>c,frontMatter:()=>s,metadata:()=>a,toc:()=>d});var i=t(85893),r=t(11151);const s={description:"Implementing popular sorting algorithms in Python",tags:["Sorting","Data Structures","Algorithms","Tutorial","Python"]},o="A Guide to Sorting",a={id:"data-structure/sorting-popular",title:"A Guide to Sorting",description:"Implementing popular sorting algorithms in Python",source:"@site/docs/data-structure/sorting-popular.md",sourceDirName:"data-structure",slug:"/data-structure/sorting-popular",permalink:"/portfolio/docs/data-structure/sorting-popular",draft:!1,unlisted:!1,tags:[{label:"Sorting",permalink:"/portfolio/docs/tags/sorting"},{label:"Data Structures",permalink:"/portfolio/docs/tags/data-structures"},{label:"Algorithms",permalink:"/portfolio/docs/tags/algorithms"},{label:"Tutorial",permalink:"/portfolio/docs/tags/tutorial"},{label:"Python",permalink:"/portfolio/docs/tags/python"}],version:"current",frontMatter:{description:"Implementing popular sorting algorithms in Python",tags:["Sorting","Data Structures","Algorithms","Tutorial","Python"]},sidebar:"tutorialSidebar",previous:{title:"Data Structures",permalink:"/portfolio/docs/data-structure/"},next:{title:"Exploratory Data Analysis",permalink:"/portfolio/docs/eda/"}},l={},d=[{value:"1. Introduction",id:"1-introduction",level:2},{value:"Why Sort?",id:"why-sort",level:3},{value:"Code Quest Begins",id:"code-quest-begins",level:3},{value:"2. Bubble Sort",id:"2-bubble-sort",level:2},{value:"Pythonic Bubbles",id:"pythonic-bubbles",level:3},{value:"3. Merge Sort",id:"3-merge-sort",level:2},{value:"Pythonic Tea Ceremony",id:"pythonic-tea-ceremony",level:3},{value:"4. Insertion Sort",id:"4-insertion-sort",level:2},{value:"Pythonic Card Tricks",id:"pythonic-card-tricks",level:3},{value:"5. Quick Sort",id:"5-quick-sort",level:2},{value:"Pythonic Dance Moves",id:"pythonic-dance-moves",level:3},{value:"6. Heap Sort",id:"6-heap-sort",level:2},{value:"Pythonic Library Stacks",id:"pythonic-library-stacks",level:3},{value:"7. Tim Sort",id:"7-tim-sort",level:2},{value:"Pythonic Incantations",id:"pythonic-incantations",level:3},{value:"8. Radix Sort",id:"8-radix-sort",level:2},{value:"Pythonic Incantations",id:"pythonic-incantations-1",level:3},{value:"9. When to Use Which Sorting Algorithm",id:"9-when-to-use-which-sorting-algorithm",level:2},{value:"The Sorting Compass",id:"the-sorting-compass",level:3},{value:"Decision-Making Elixirs",id:"decision-making-elixirs",level:3},{value:"Parting Spell",id:"parting-spell",level:3}];function h(e){const n={admonition:"admonition",code:"code",h1:"h1",h2:"h2",h3:"h3",li:"li",ol:"ol",p:"p",pre:"pre",strong:"strong",ul:"ul",...(0,r.a)(),...e.components};return(0,i.jsxs)(i.Fragment,{children:[(0,i.jsx)(n.h1,{id:"a-guide-to-sorting",children:"A Guide to Sorting"}),"\n",(0,i.jsx)(n.h2,{id:"1-introduction",children:"1. Introduction"}),"\n",(0,i.jsx)(n.p,{children:"Sorting is like tidying up your room after a wild party. But instead of picking up empty cups and crumpled napkins, we're wrangling data. Imagine a chaotic array of numbers, letters, or even mystical runes (hey, we don't judge). Our mission? To transform this unruly mess into a neat, orderly sequence."}),"\n",(0,i.jsx)(n.h3,{id:"why-sort",children:"Why Sort?"}),"\n",(0,i.jsx)(n.p,{children:"Before we dive into the nitty-gritty, let's ponder the cosmic significance of sorting. Why bother? Well, my fellow data voyagers, sorting isn't just about aesthetics. It's about efficiency, searchability, and making sense of the universe (okay, maybe not the last part)."}),"\n",(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Searchability:"}),' Imagine a phone book (remember those?). Without sorting, finding "Zachary Zebra" would involve flipping through pages like a caffeinated squirrel. Sorted? Bam!']}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Efficiency:"})," Algorithms crave efficiency like a cat craves sunbeams. Sorting helps us optimize search, merge datasets, and prepare data for other operations. Efficiency is our golden ticket to algorithmic stardom."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"code-quest-begins",children:"Code Quest Begins"}),"\n",(0,i.jsx)(n.p,{children:"In the upcoming sections, we'll delve into sorting algorithms. We'll write Python code and explore their strengths, weaknesses, and secret handshakes. Brace yourselves for practical tips, eureka moments, and maybe a meme or two."}),"\n",(0,i.jsx)(n.p,{children:"So grab your favorite caffeinated beverage (mine's a double-shot espresso), open your Python IDE, and let's embark on this sorting odyssey. Remember, sorting isn't just about order\u2014it's about finding beauty in chaos. \ud83c\udf1f\ud83d\udd0d\ud83d\udc0d"}),"\n",(0,i.jsx)(n.h2,{id:"2-bubble-sort",children:"2. Bubble Sort"}),"\n",(0,i.jsx)(n.p,{children:"Bubble Sort's dance moves are simple:"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Bubble Up:"})," Imagine bubbles rising in a fizzy soda. Similarly, Bubble Sort compares adjacent elements in your list. If they're out of order, it swaps them."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Repeat:"}),' Keep bubbling up the list until everything settles into place. It\'s like a never-ending game of "Is this in the right order?"']}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"pythonic-bubbles",children:"Pythonic Bubbles"}),"\n",(0,i.jsx)(n.p,{children:"Let's brew some Python tea. Here's our Bubble Sort recipe:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:'title="Python"',children:'def bubble_sort(arr):\n    n = len(arr)\n    for i in range(n):\n        # Last i elements are already sorted\n        for j in range(0, n - i - 1):\n            if arr[j] > arr[j + 1]:\n                # Swap adjacent elements\n                arr[j], arr[j + 1] = arr[j + 1], arr[j]\n\n\n# Example usage\nmy_list = [5, 2, 9, 1, 5, 6]\nbubble_sort(my_list)\nprint("Sorted list:", my_list)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{children:"Sorted list: [1, 2, 5, 5, 6, 9]\n"})}),"\n",(0,i.jsx)(n.admonition,{type:"tip",children:(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Bubble Sort isn't the fastest kid on the block. It's like the tortoise in the sorting race. But hey, it gets the job done eventually."}),"\n",(0,i.jsx)(n.li,{children:"If your list is mostly sorted already, Bubble Sort performs better. It's like tidying up a room that's already fairly neat."}),"\n"]})}),"\n",(0,i.jsx)(n.h2,{id:"3-merge-sort",children:"3. Merge Sort"}),"\n",(0,i.jsxs)(n.p,{children:["Merge Sort follows a simple yet profound philosophy: ",(0,i.jsx)(n.strong,{children:"divide and conquer"}),". It's like splitting a giant puzzle into smaller, solvable pieces. Here's the playbook:"]}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Divide:"})," Take your unsorted list and split it into halves. Keep doing this until you have tiny, sorted sublists. Think of it as a peaceful tea ceremony\u2014each cup represents a sublist."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Conquer:"})," Now, merge those sublists back together. But not haphazardly! Merge them in a way that maintains order. Imagine arranging tea cups by size\u2014small to large."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Repeat:"})," Keep dividing, conquering, and merging until your entire list is sorted. It's like assembling a thousand origami cranes\u2014one fold at a time."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"pythonic-tea-ceremony",children:"Pythonic Tea Ceremony"}),"\n",(0,i.jsx)(n.p,{children:"Let's brew some Python code, shall we? Our teapot (read: Python IDE) awaits:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:'title="Python"',children:'def merge_sort(arr):\n    if len(arr) <= 1:\n        return arr  # Already sorted or empty\n\n    # Divide: Split the list into halves\n    mid = len(arr) // 2\n    left_half = arr[:mid]\n    right_half = arr[mid:]\n\n    # Recursively sort each half\n    left_sorted = merge_sort(left_half)\n    right_sorted = merge_sort(right_half)\n\n    # Conquer: Merge the sorted halves\n    return merge(left_sorted, right_sorted)\n\n\ndef merge(left, right):\n    result = []\n    left_idx, right_idx = 0, 0\n\n    while left_idx < len(left) and right_idx < len(right):\n        if left[left_idx] < right[right_idx]:\n            result.append(left[left_idx])\n            left_idx += 1\n        else:\n            result.append(right[right_idx])\n            right_idx += 1\n\n    # Handle remaining elements (if any)\n    result.extend(left[left_idx:])\n    result.extend(right[right_idx:])\n    return result\n\n\n# Example usage\nmy_list = [5, 2, 9, 1, 5, 6]\nsorted_list = merge_sort(my_list)\nprint("Sorted list:", sorted_list)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{children:"Sorted list: [1, 2, 5, 5, 6, 9]\n"})}),"\n",(0,i.jsx)(n.admonition,{type:"tip",children:(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Merge Sort is like a calm river flowing through your data. It's stable, predictable, and won't surprise you with sudden rapids."}),"\n",(0,i.jsx)(n.li,{children:"Its time complexity? A soothing O(n log n). Perfect for contemplative evenings."}),"\n"]})}),"\n",(0,i.jsx)(n.h2,{id:"4-insertion-sort",children:"4. Insertion Sort"}),"\n",(0,i.jsx)(n.p,{children:"Imagine you're holding a deck of playing cards. Your goal? Arrange them in ascending order. Here's how Insertion Sort plays the game:"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Virtual Split:"})," The array is virtually split into two parts: the ",(0,i.jsx)(n.strong,{children:"sorted"})," and the ",(0,i.jsx)(n.strong,{children:"unsorted"}),". The sorted part starts empty, like an empty card table."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Pick and Place:"})," From the unsorted part, pick a card (element) and place it in its correct position within the sorted part. It's like adding a card to your sorted hand."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Repeat:"})," Keep picking cards from the unsorted part and inserting them into the sorted part. Each insertion expands the sorted section."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"pythonic-card-tricks",children:"Pythonic Card Tricks"}),"\n",(0,i.jsx)(n.p,{children:"Let's shuffle our Python deck and perform some card magic (no top hats required):"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:'title="Python"',children:'def insertion_sort(arr):\n    for i in range(1, len(arr)):\n        key = arr[i]  # The card we\'re inserting\n        j = i - 1\n\n        # Compare with cards in the sorted part\n        while j >= 0 and arr[j] > key:\n            arr[j + 1] = arr[j]  # Shift larger cards to the right\n            j -= 1\n\n        arr[j + 1] = key  # Insert the card in its correct position\n\n\n# Example usage\nmy_cards = [5, 2, 9, 1, 5, 6]\ninsertion_sort(my_cards)\nprint("Sorted cards:", my_cards)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{children:"Sorted cards: [1, 2, 5, 5, 6, 9]\n"})}),"\n",(0,i.jsx)(n.admonition,{type:"tip",children:(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Insertion Sort is like arranging cards in your hand. It's simple, stable, and patient."}),"\n",(0,i.jsx)(n.li,{children:"Use it when your list is small or nearly sorted. It's not the fastest, but it's reliable."}),"\n"]})}),"\n",(0,i.jsx)(n.h2,{id:"5-quick-sort",children:"5. Quick Sort"}),"\n",(0,i.jsxs)(n.p,{children:["Quick Sort's secret sauce? ",(0,i.jsx)(n.strong,{children:"Pivoting"}),". Imagine a dance floor where elements groove to the beat. Here's how it works:"]}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Choose a Pivot:"})," Our DJ (that's you) picks an element from the list. This chosen one is the pivot\u2014the life of the party."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Partitioning:"})," Now, let's create two groups: those who dance better than the pivot and those who need a few lessons. Elements greater than the pivot join the cool kids' club, while the rest form their own clique."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Repeat:"})," Recursively apply Quick Sort to both groups. It's like splitting the dance floor into smaller circles, each with its own pivot."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"pythonic-dance-moves",children:"Pythonic Dance Moves"}),"\n",(0,i.jsx)(n.p,{children:"Grab your Python dancing shoes (they're neon green, trust me) and let's write some code:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:'title="Python"',children:'def quick_sort(arr):\n    if len(arr) <= 1:\n        return arr  # Already sorted or empty\n\n    pivot = arr[len(arr) // 2]  # Choose a pivot\n    left, equal, right = [], [], []\n\n    for element in arr:\n        if element < pivot:\n            left.append(element)\n        elif element == pivot:\n            equal.append(element)\n        else:\n            right.append(element)\n\n    # Recursively sort the partitions\n    return quick_sort(left) + equal + quick_sort(right)\n\n\n# Example usage\nmy_list = [8, 3, 1, 6, 4, 7]\nsorted_list = quick_sort(my_list)\nprint("Sorted list:", sorted_list)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{children:"Sorted list: [1, 3, 4, 6, 7, 8]\n"})}),"\n",(0,i.jsx)(n.admonition,{type:"tip",children:(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Quick Sort is like a DJ remixing your playlist. It's fast, unpredictable, and occasionally drops the bass (or swaps elements)."}),"\n",(0,i.jsx)(n.li,{children:"Its average-case time complexity? A lightning-quick O(n log n). Perfect for impromptu dance-offs."}),"\n"]})}),"\n",(0,i.jsx)(n.h2,{id:"6-heap-sort",children:"6. Heap Sort"}),"\n",(0,i.jsxs)(n.p,{children:["Imagine a library with neatly stacked books. Each book has a priority (think Dewey Decimal System). Heap Sort builds a ",(0,i.jsx)(n.strong,{children:"binary heap"}),", which is like a well-behaved tree. Here's the hushed process:"]}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Heapify:"}),' Arrange your unsorted list into a binary heap. It\'s like whispering to the books, "Find your proper place, my dear."']}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Sorting Act:"})," Extract the root (highest priority) element and replace it with the last leaf. Then, restore the heap property. Repeat until the entire heap is sorted. It's like a silent ballet\u2014graceful and efficient."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"pythonic-library-stacks",children:"Pythonic Library Stacks"}),"\n",(0,i.jsx)(n.p,{children:"Let's tiptoe through the code stacks (no shushing required):"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:'title="Python"',children:'def heapify(arr, n, root):\n    largest = root\n    left_child = 2 * root + 1\n    right_child = 2 * root + 2\n\n    if left_child < n and arr[left_child] > arr[largest]:\n        largest = left_child\n\n    if right_child < n and arr[right_child] > arr[largest]:\n        largest = right_child\n\n    if largest != root:\n        arr[root], arr[largest] = arr[largest], arr[root]\n        heapify(arr, n, largest)\n\n\ndef heap_sort(arr):\n    n = len(arr)\n\n    # Build a max heap\n    for i in range(n // 2 - 1, -1, -1):\n        heapify(arr, n, i)\n\n    # Extract elements one by one\n    for i in range(n - 1, 0, -1):\n        arr[i], arr[0] = arr[0], arr[i]  # Swap root with last element\n        heapify(arr, i, 0)\n\n\n# Example usage\nmy_list = [12, 11, 13, 5, 6, 7]\nheap_sort(my_list)\nprint("Sorted list:", my_list)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{children:"Sorted list: [5, 6, 7, 11, 12, 13]\n"})}),"\n",(0,i.jsx)(n.admonition,{type:"tip",children:(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Heap Sort is like a librarian who alphabetizes books without making a sound. Its time complexity? A hushed O(n log n)."}),"\n",(0,i.jsx)(n.li,{children:"Use it when you need a sorted list but don't want to disturb the other algorithms at the party."}),"\n"]})}),"\n",(0,i.jsx)(n.h2,{id:"7-tim-sort",children:"7. Tim Sort"}),"\n",(0,i.jsx)(n.p,{children:"Tim Sort isn't your average sorting hero. It's a hybrid, a fusion of ancient wisdom and modern pragmatism. Picture this:"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Merge Sort Meets Insertion Sort:"})," Tim Sort marries the elegance of Merge Sort with the practicality of Insertion Sort. It's like inviting Gandalf and Hermione to the same tea party."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Gallop, Gallop, Gallop:"})," Tim Sort gallops through your list, sniffing out order like a seasoned detective. It's adaptive\u2014gentle with nearly sorted data, fierce when chaos reigns."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"The Power of Runs:"})," Tim Sort breaks your list into ",(0,i.jsx)(n.strong,{children:"runs"}),"\u2014chunks of sorted or semi-sorted elements. Then it merges these runs, creating harmony in the chaos."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"pythonic-incantations",children:"Pythonic Incantations"}),"\n",(0,i.jsx)(n.p,{children:"Let's weave some Python spells (no wands required, just code):"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:'title="Python"',children:'def tim_sort(arr):\n    min_run = 32  # Minimum size of a run\n    n = len(arr)\n\n    # Create runs\n    for start in range(0, n, min_run):\n        end = min(start + min_run, n)\n        insertion_sort(arr, start, end)\n\n    # Merge runs\n    size = min_run\n    while size < n:\n        for start in range(0, n, 2 * size):\n            mid = min(start + size, n)\n            end = min(start + 2 * size, n)\n            merge(arr, start, mid, end)\n        size *= 2\n\n\ndef insertion_sort(arr, start, end):\n    # Insertion Sort within a run\n    for i in range(start + 1, end):\n        key = arr[i]\n        j = i - 1\n        while j >= start and arr[j] > key:\n            arr[j + 1] = arr[j]\n            j -= 1\n        arr[j + 1] = key\n\n\ndef merge(arr, start, mid, end):\n    # Merge two runs\n    left, right = arr[start:mid], arr[mid:end]\n    i, j, k = 0, 0, start\n\n    while i < len(left) and j < len(right):\n        if left[i] <= right[j]:\n            arr[k] = left[i]\n            i += 1\n        else:\n            arr[k] = right[j]\n            j += 1\n        k += 1\n\n    arr[k:end] = left[i:] + right[j:]\n\n\n# Example usage\nmy_list = [10, 7, 3, 8, 5, 1]\ntim_sort(my_list)\nprint("Sorted list:", my_list)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{children:"Sorted list: [1, 3, 5, 7, 8, 10]\n"})}),"\n",(0,i.jsx)(n.admonition,{type:"tip",children:(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Tim Sort is like a seasoned traveler who adapts to any terrain. Its time complexity? A mystical O(n log n)."}),"\n",(0,i.jsx)(n.li,{children:"Use it when you need stability and efficiency."}),"\n"]})}),"\n",(0,i.jsx)(n.h2,{id:"8-radix-sort",children:"8. Radix Sort"}),"\n",(0,i.jsxs)(n.p,{children:["Radix Sort doesn't play by the usual rules. It's not concerned with comparisons or pivots. Instead, it focuses on the ",(0,i.jsx)(n.strong,{children:"magical digits"})," within numbers. Here's the incantation:"]}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Least Significant Digit (LSD) Variant:"})," Imagine sorting playing cards by their suit. Radix Sort starts with the rightmost digit (the least significant) and arranges the cards into buckets (one for each digit). Then it repeats the process for the tens place, hundreds place, and so on. Like a patient librarian organizing a chaotic bookshelf."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Most Significant Digit (MSD) Variant:"})," This time, Radix Sort begins with the leftmost digit (the most significant). It's like sorting books by their genre\u2014first all the mysteries, then the romances, and so forth. The process continues recursively until the entire list is sorted."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"pythonic-incantations-1",children:"Pythonic Incantations"}),"\n",(0,i.jsx)(n.p,{children:"Let's summon our Python spellbook and write some code runes:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:'title="Python"',children:'def radix_sort(arr):\n    max_digit_count = len(str(max(arr)))  # Maximum number of digits\n\n    for digit_position in range(max_digit_count):\n        buckets = [[] for _ in range(10)]  # Create 10 magical buckets\n\n        for num in arr:\n            digit = (num // 10**digit_position) % 10\n            buckets[digit].append(num)\n\n        arr = [num for bucket in buckets for num in bucket]\n\n    return arr\n\n\n# Example usage\nmy_numbers = [123, 45, 789, 12, 567, 890]\nsorted_numbers = radix_sort(my_numbers)\nprint("Sorted numbers:", sorted_numbers)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{children:"Sorted numbers: [12, 45, 123, 567, 789, 890]\n"})}),"\n",(0,i.jsx)(n.admonition,{type:"tip",children:(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Radix Sort is like a numerologist arranging cosmic energies. Its time complexity? A mystical O(nk), where k is the number of digits."}),"\n",(0,i.jsx)(n.li,{children:"Use it when you want to sort numbers without comparing their souls (I mean values)."}),"\n"]})}),"\n",(0,i.jsx)(n.h2,{id:"9-when-to-use-which-sorting-algorithm",children:"9. When to Use Which Sorting Algorithm"}),"\n",(0,i.jsx)(n.p,{children:"Dear data voyagers, let's don our sorting hats and discuss the art of algorithmic decision-making. Choosing the right sorting spell for the job is like selecting the perfect wand\u2014essential for a magical outcome. \ud83c\udf1f\ud83d\udd2e"}),"\n",(0,i.jsx)(n.h3,{id:"the-sorting-compass",children:"The Sorting Compass"}),"\n",(0,i.jsx)(n.p,{children:"Before we dive into specifics, let's consult our sorting compass:"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Bubble Sort:"})," Use it when your list is small or nearly sorted. It's like tidying up a room that's already fairly neat. But beware\u2014it's not the fastest broomstick in the sky."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Merge Sort:"})," When stability and predictability matter, Merge Sort is your go-to."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Insertion Sort:"})," It's like arranging cards in your hand. Use it when your list is small or nearly sorted. It's not the fastest, but it's reliable."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Quick Sort:"})," When you need raw speed, summon Quick Sort. Just keep an eye on its worst-case mood swings\u2014it can trip over its own shoelaces."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Heap Sort:"})," Heap Sort builds a binary heap, using space efficiently. Use it when you want reliability without fanfare."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Tim Sort:"})," Tim Sort combines Merge Sort and Insertion Sort. It's adaptive and stable. When in doubt, trust Tim\u2014it's like having a Gandalf-Hermione hybrid on your team."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Radix Sort:"})," Radix Sort sorts numbers by their digits. Use it when you want to sort without comparing values."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"decision-making-elixirs",children:"Decision-Making Elixirs"}),"\n",(0,i.jsx)(n.p,{children:"Here are some practical guidelines:"}),"\n",(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Small Lists:"})," For tiny lists, Bubble Sort is the friendly neighbor who pops over for a cup of sugar. It's simple, but don't invite it to a royal ball (large lists)."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Nearly Sorted Data:"})," Merge Sort is the calm river flowing through your data garden. It's stable and predictable. Use it when your data is already in a zen-like state."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Speed Demons:"})," Quick Sort is the adrenaline junkie. When you need raw speed, Quick Sort pivots and dances faster than a caffeinated squirrel. Just keep an eye on its worst-case mood swings."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Binary Heap Magic:"}),' Heap Sort is the minimalist librarian. It builds a binary heap, using space efficiently. When memory is scarce, Heap Sort whispers, "Fear not, I won\'t clutter your RAM."']}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Pythonic Charm:"})," Tim Sort is Python's own. It adapts, blends, and knows the best pizza joints. When in doubt, trust Tim\u2014it's seen more magic than Merlin."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Digit Sorcery:"})," Radix Sort sorts by digits. It's like numerology for numbers. Use it when you want to sort without comparing values."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Card Arrangement:"})," Insertion Sort is the gentle card arranger. Use it when your list is small or nearly sorted. It's not the fastest, but it's reliable."]}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"parting-spell",children:"Parting Spell"}),"\n",(0,i.jsx)(n.p,{children:"So, fellow enchanters, choose wisely. Whether you're organizing a potion cabinet or sorting a massive dataset, remember: each algorithm has its own wand, and the right one can turn chaos into enchantment. \ud83c\udf1f\ud83d\udd2e\ud83d\udcdc"}),"\n",(0,i.jsx)(n.p,{children:"Now, go forth and sort\u2014may your lists be ordered!"})]})}function c(e={}){const{wrapper:n}={...(0,r.a)(),...e.components};return n?(0,i.jsx)(n,{...e,children:(0,i.jsx)(h,{...e})}):h(e)}},11151:(e,n,t)=>{t.d(n,{Z:()=>a,a:()=>o});var i=t(67294);const r={},s=i.createContext(r);function o(e){const n=i.useContext(s);return i.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function a(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(r):e.components||r:o(e.components),i.createElement(s.Provider,{value:n},e.children)}}}]);
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similarity index 98%
rename from assets/js/5cffd2bd.f905ad2d.js
rename to assets/js/5cffd2bd.b9f5e1a0.js
index 4a0c854a..41c53932 100644
--- a/assets/js/5cffd2bd.f905ad2d.js
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@@ -1 +1 @@
-"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[7862],{35417:(e,t,n)=>{n.r(t),n.d(t,{assets:()=>c,contentTitle:()=>r,default:()=>u,frontMatter:()=>i,metadata:()=>l,toc:()=>s});var o=n(85893),a=n(11151);const i={title:"Get-Rich-Quick Schemes are a Bad Idea",authors:["mori"],tags:["Data Science","Machine Learning","Get Rich Quick","Mythology","AI"]},r=void 0,l={permalink:"/portfolio/blog/get-rich-quick",source:"@site/blog/get-rich-quick.md",title:"Get-Rich-Quick Schemes are a Bad Idea",description:"Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there\u2019s no shortcut to success in data science.",date:"2024-04-05T14:49:00.000Z",formattedDate:"April 5, 2024",tags:[{label:"Data Science",permalink:"/portfolio/blog/tags/data-science"},{label:"Machine Learning",permalink:"/portfolio/blog/tags/machine-learning"},{label:"Get Rich Quick",permalink:"/portfolio/blog/tags/get-rich-quick"},{label:"Mythology",permalink:"/portfolio/blog/tags/mythology"},{label:"AI",permalink:"/portfolio/blog/tags/ai"}],readingTime:7.24,hasTruncateMarker:!0,authors:[{name:"Morteza Hosseini",title:"Data scientist / ML engineer",key:"mori"}],frontMatter:{title:"Get-Rich-Quick Schemes are a Bad Idea",authors:["mori"],tags:["Data Science","Machine Learning","Get Rich Quick","Mythology","AI"]},unlisted:!1,prevItem:{title:"Exploring AI Ethics: 15 Key Q&As",permalink:"/portfolio/blog/ethics"},nextItem:{title:"Advanced Guidelines for ML Model Training",permalink:"/portfolio/blog/guideline-ml"}},c={authorsImageUrls:[void 0]},s=[];function h(e){const t={p:"p",...(0,a.a)(),...e.components};return(0,o.jsxs)(o.Fragment,{children:[(0,o.jsx)(t.p,{children:"Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there\u2019s no shortcut to success in data science."}),"\n",(0,o.jsx)(t.p,{children:"Let\u2019s face it, we all want to be successful and financially stable. And with the booming field of data science and machine learning, it\u2019s tempting to believe that we can achieve our financial dreams by simply learning a few skills and jumping on the bandwagon. Unfortunately, the reality is far from that."}),"\n",(0,o.jsx)(t.p,{children:"Here, we\u2019ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let\u2019s debunk some myths!"})]})}function u(e={}){const{wrapper:t}={...(0,a.a)(),...e.components};return t?(0,o.jsx)(t,{...e,children:(0,o.jsx)(h,{...e})}):h(e)}},11151:(e,t,n)=>{n.d(t,{Z:()=>l,a:()=>r});var o=n(67294);const a={},i=o.createContext(a);function r(e){const t=o.useContext(i);return o.useMemo((function(){return"function"==typeof e?e(t):{...t,...e}}),[t,e])}function l(e){let t;return t=e.disableParentContext?"function"==typeof e.components?e.components(a):e.components||a:r(e.components),o.createElement(i.Provider,{value:t},e.children)}}}]);
\ No newline at end of file
+"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[7862],{35417:(e,t,n)=>{n.r(t),n.d(t,{assets:()=>c,contentTitle:()=>r,default:()=>u,frontMatter:()=>i,metadata:()=>l,toc:()=>s});var o=n(85893),a=n(11151);const i={title:"Get-Rich-Quick Schemes are a Bad Idea",authors:["mori"],tags:["Data Science","Machine Learning","Get Rich Quick","Mythology","AI"]},r=void 0,l={permalink:"/portfolio/blog/get-rich-quick",source:"@site/blog/get-rich-quick.md",title:"Get-Rich-Quick Schemes are a Bad Idea",description:"Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there\u2019s no shortcut to success in data science.",date:"2024-04-09T10:32:08.000Z",formattedDate:"April 9, 2024",tags:[{label:"Data Science",permalink:"/portfolio/blog/tags/data-science"},{label:"Machine Learning",permalink:"/portfolio/blog/tags/machine-learning"},{label:"Get Rich Quick",permalink:"/portfolio/blog/tags/get-rich-quick"},{label:"Mythology",permalink:"/portfolio/blog/tags/mythology"},{label:"AI",permalink:"/portfolio/blog/tags/ai"}],readingTime:7.24,hasTruncateMarker:!0,authors:[{name:"Morteza Hosseini",title:"Data scientist / ML engineer",key:"mori"}],frontMatter:{title:"Get-Rich-Quick Schemes are a Bad Idea",authors:["mori"],tags:["Data Science","Machine Learning","Get Rich Quick","Mythology","AI"]},unlisted:!1,prevItem:{title:"Exploring AI Ethics: 15 Key Q&As",permalink:"/portfolio/blog/ethics"},nextItem:{title:"Advanced Guidelines for ML Model Training",permalink:"/portfolio/blog/guideline-ml"}},c={authorsImageUrls:[void 0]},s=[];function h(e){const t={p:"p",...(0,a.a)(),...e.components};return(0,o.jsxs)(o.Fragment,{children:[(0,o.jsx)(t.p,{children:"Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there\u2019s no shortcut to success in data science."}),"\n",(0,o.jsx)(t.p,{children:"Let\u2019s face it, we all want to be successful and financially stable. And with the booming field of data science and machine learning, it\u2019s tempting to believe that we can achieve our financial dreams by simply learning a few skills and jumping on the bandwagon. Unfortunately, the reality is far from that."}),"\n",(0,o.jsx)(t.p,{children:"Here, we\u2019ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let\u2019s debunk some myths!"})]})}function u(e={}){const{wrapper:t}={...(0,a.a)(),...e.components};return t?(0,o.jsx)(t,{...e,children:(0,o.jsx)(h,{...e})}):h(e)}},11151:(e,t,n)=>{n.d(t,{Z:()=>l,a:()=>r});var o=n(67294);const a={},i=o.createContext(a);function r(e){const t=o.useContext(i);return o.useMemo((function(){return"function"==typeof e?e(t):{...t,...e}}),[t,e])}function l(e){let t;return t=e.disableParentContext?"function"==typeof e.components?e.components(a):e.components||a:r(e.components),o.createElement(i.Provider,{value:t},e.children)}}}]);
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diff --git a/assets/js/78117feb.337928a4.js b/assets/js/78117feb.bde2cf36.js
similarity index 99%
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rename to assets/js/78117feb.bde2cf36.js
index d712d547..c708d9f9 100644
--- a/assets/js/78117feb.337928a4.js
+++ b/assets/js/78117feb.bde2cf36.js
@@ -1 +1 @@
-"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[7399],{98762:(e,n,i)=>{i.r(n),i.d(n,{assets:()=>l,contentTitle:()=>s,default:()=>d,frontMatter:()=>a,metadata:()=>o,toc:()=>c});var t=i(85893),r=i(11151);const a={title:"Advanced Guidelines for ML Model Training",authors:["mori"],tags:["Machine Learning","Model Training","Guidelines","Data Science","Neural Networks"]},s=void 0,o={permalink:"/portfolio/blog/guideline-ml",source:"@site/blog/guideline-ml.md",title:"Advanced Guidelines for ML Model Training",description:"Understanding Model Training",date:"2024-04-05T14:49:00.000Z",formattedDate:"April 5, 2024",tags:[{label:"Machine Learning",permalink:"/portfolio/blog/tags/machine-learning"},{label:"Model Training",permalink:"/portfolio/blog/tags/model-training"},{label:"Guidelines",permalink:"/portfolio/blog/tags/guidelines"},{label:"Data Science",permalink:"/portfolio/blog/tags/data-science"},{label:"Neural Networks",permalink:"/portfolio/blog/tags/neural-networks"}],readingTime:12.375,hasTruncateMarker:!0,authors:[{name:"Morteza Hosseini",title:"Data scientist / ML engineer",key:"mori"}],frontMatter:{title:"Advanced Guidelines for ML Model Training",authors:["mori"],tags:["Machine Learning","Model Training","Guidelines","Data Science","Neural Networks"]},unlisted:!1,prevItem:{title:"Get-Rich-Quick Schemes are a Bad Idea",permalink:"/portfolio/blog/get-rich-quick"},nextItem:{title:"Introducing Keras Core",permalink:"/portfolio/blog/keras-core"}},l={authorsImageUrls:[void 0]},c=[{value:"Understanding Model Training",id:"understanding-model-training",level:2},{value:"Preparing your data",id:"preparing-your-data",level:2},{value:"Writing Efficient Training Loops for Neural Networks",id:"writing-efficient-training-loops-for-neural-networks",level:2},{value:"Optimizing Hyperparameters",id:"optimizing-hyperparameters",level:2},{value:"Regularization Techniques",id:"regularization-techniques",level:2},{value:"Choosing the Right Loss Functions",id:"choosing-the-right-loss-functions",level:2},{value:"Crafting Custom Neural Architectures",id:"crafting-custom-neural-architectures",level:2},{value:"The Power of Combining Models",id:"the-power-of-combining-models",level:2},{value:"Leveraging Callbacks for Training Insights",id:"leveraging-callbacks-for-training-insights",level:2},{value:"Staying Updated with Research and Trends",id:"staying-updated-with-research-and-trends",level:2}];function h(e){const n={a:"a",em:"em",h2:"h2",li:"li",p:"p",strong:"strong",ul:"ul",...(0,r.a)(),...e.components};return(0,t.jsxs)(t.Fragment,{children:[(0,t.jsx)(n.h2,{id:"understanding-model-training",children:"Understanding Model Training"}),"\n",(0,t.jsx)(n.p,{children:"Welcome to the captivating realm of machine learning, where algorithms breathe life into data and unveil patterns that were once hidden in the shadows. Before we dive into the intricate dance of code and data, let\u2019s take a moment to understand the essence of model training."}),"\n",(0,t.jsx)(n.p,{children:"Imagine yourself as an artisan, crafting a masterpiece from raw materials. Just as a painter starts with a blank canvas, you begin with a dataset rich in information. This dataset is your palette, and your model is the brush that will paint the future. \ud83c\udfa8\ud83e\udd16"}),"\n",(0,t.jsx)(n.p,{children:"Model training is the process of imbuing your creation with the ability to learn from data and make predictions. Just as a symphony conductor guides each musician to play in harmony, you guide your model through the data."}),"\n",(0,t.jsx)(n.h2,{id:"preparing-your-data",children:"Preparing your data"}),"\n",(0,t.jsx)(n.p,{children:"In the grand symphony of model training, data preprocessing plays the role of tuning each instrument before the performance. Just as a musician wouldn\u2019t play with a broken string, your model won\u2019t perform well with unrefined data. \ud83c\udfbb\ud83c\udfbc"}),"\n",(0,t.jsx)(n.p,{children:"Let\u2019s embark on the journey of transforming raw data into a harmonious dataset:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Data Cleaning:"})," Begin by scrubbing your data clean. Detect and handle ",(0,t.jsx)(n.em,{children:"missing values"}),", ",(0,t.jsx)(n.em,{children:"outliers"}),", and inconsistencies that could disrupt your model\u2019s rhythm."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Feature Engineering:"})," Craft a melody of features that resonate with your model\u2019s ",(0,t.jsx)(n.em,{children:"objective"}),". Engineer meaningful features that capture the essence of your problem domain."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Scaling and Normalization:"})," Ensure that your features are on the same scale, allowing your model to converge faster and avoid undue influence from dominant features."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Encoding:"})," Convert ",(0,t.jsx)(n.em,{children:"categorical"})," variables into a format that the model can understand. Whether it\u2019s ",(0,t.jsx)(n.em,{children:"one-hot encoding"}),", ",(0,t.jsx)(n.em,{children:"label encoding"}),", or other techniques, create a harmonious blend of categorical and numerical features."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Train-Validation-Test Split:"})," Divide your dataset into training, validation, and test sets. Each has a unique role in refining your model\u2019s performance."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Data Augmentation:"})," For image and sequence data, explore the realm of data augmentation. This technique introduces variety into your training data, enabling your model to ",(0,t.jsx)(n.em,{children:"generalize better"}),"."]}),"\n"]}),"\n",(0,t.jsx)(n.h2,{id:"writing-efficient-training-loops-for-neural-networks",children:"Writing Efficient Training Loops for Neural Networks"}),"\n",(0,t.jsx)(n.p,{children:"Now that your dataset is preprocessed, it\u2019s time to step into the realm of code and write the enchanting spells that will bring your model to life. Just as a playwright crafts a compelling script, you\u2019ll code a training loop that guides your model\u2019s evolution:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Initialization:"})," Set the stage by initializing your model\u2019s architecture, loss function, and optimizer. Think of this as creating the canvas upon which your model will paint its predictions."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Epoch:"})," Enter the world of epochs, where each iteration weaves a new chapter of learning. Design your training loop to iterate through your dataset, presenting patterns to your model that it can decipher."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Batch:"})," Instead of presenting your model with the entire dataset at once, serve it bite-sized portions called batches. This ",(0,t.jsx)(n.em,{children:"accelerates learning"})," and ",(0,t.jsx)(n.em,{children:"reduces memory consumption"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Backpropagation:"})," In this step, your model learns from its mistakes by adjusting its internal parameters. Gradient descent guides it closer to accurate predictions with each step."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Validation:"})," Don\u2019t forget your validation set! After each epoch, serenade your model with the validation data to gauge its performance and ",(0,t.jsx)(n.em,{children:"prevent overfitting"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Early Stopping:"})," A magical trick to avoid prolonged training. If your model\u2019s performance on the validation set stagnates or worsens, elegantly halt the training and ",(0,t.jsx)(n.em,{children:"save resources"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Checkpoint:"})," Cast a checkpoint spell to save your model\u2019s progress during training. If anything goes awry, you can always ",(0,t.jsx)(n.em,{children:"pick up where you left off"}),"."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"As you craft your training loop, envision yourself as a conductor guiding your model towards mastery. Each line of code is a note in the symphony of learning, harmonizing to create a melody of intelligence. Just as a conductor brings out the best from each musician, you\u2019ll coax out the finest predictions from your model, one iteration at a time. \ud83c\udfb5\ud83d\udd25"}),"\n",(0,t.jsx)(n.h2,{id:"optimizing-hyperparameters",children:"Optimizing Hyperparameters"}),"\n",(0,t.jsx)(n.p,{children:"As you embark on the journey of machine learning, you\u2019ll encounter hyperparameters\u2014settings that wield immense influence over your model\u2019s performance. Much like a blacksmith forging a mighty sword, you\u2019ll shape these hyperparameters to enhance your model\u2019s power:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Hyperspace:"})," Dive into the realm of hyperparameters\u2014learning rate, batch size, hidden units, and more. Each choice alters your model\u2019s trajectory, and finding the right balance is your quest."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Grid Search:"})," Equip yourself with a grid search spell to systematically explore different hyperparameter combinations. Uncover the sweet spot that ",(0,t.jsx)(n.em,{children:"maximizes"})," your model\u2019s performance."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Random Search:"})," Embark on an adventure that balances exploration and efficiency. Randomly sample hyperparameter values to discover hidden gems without exhaustive searching."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Bayesian Optimization:"})," Unveil the elegance of Bayesian optimization\u2014a smarter way to search for optimal hyperparameters. This method intelligently ",(0,t.jsx)(n.em,{children:"narrows down the search space"})," and accelerates your quest."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Regularization:"})," Beware the curse of overfitting on your hyperparameters. Just as knights wear armor for protection, regularize your hyperparameters to prevent them from ",(0,t.jsx)(n.em,{children:"fitting noise"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Automated Tuning:"})," Employ automated hyperparameter tuning libraries like ",(0,t.jsx)(n.a,{href:"https://optuna.org/",children:"Optuna"})," or ",(0,t.jsx)(n.a,{href:"https://hyperopt.github.io/hyperopt/",children:"Hyperopt"}),". These tools streamline the search for optimal hyperparameters, freeing you to focus on your model\u2019s magic."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"In your pursuit of optimal hyperparameters, imagine yourself as an alchemist blending rare ingredients. Each hyperparameter value is a crucial component, and the right blend will transform your model from good to extraordinary. Just as an alchemist\u2019s concoction holds secrets, your choice of hyperparameters will unlock the hidden potential within your model. \ud83e\uddea\u2728"}),"\n",(0,t.jsx)(n.h2,{id:"regularization-techniques",children:"Regularization Techniques"}),"\n",(0,t.jsxs)(n.p,{children:["In the intricate tapestry of machine learning, complexity can sometimes lead to chaos. Regularization techniques are your spells to tame the unruly, ensuring that your model doesn\u2019t succumb to ",(0,t.jsx)(n.em,{children:"overfitting"}),". Just as a skilled trainer guides a wild beast, you\u2019ll control the complexity of your model:"]}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Lasso and Ridge:"})," Explore the wonders of L1 and L2 regularization. These techniques add a touch of magic to your loss function, ",(0,t.jsx)(n.em,{children:"penalizing overly complex models"})," and ushering in simplicity."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Dropout:"})," Unveil the enigmatic power of dropout\u2014a technique where neurons \u201cdisappear\u201d during training. This prevents ",(0,t.jsx)(n.em,{children:"co-dependency"})," and encourages each neuron to be self-sufficient."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Batch Normalization:"})," Cast a charm that stabilizes learning by normalizing the inputs of each layer. This technique keeps your model from wandering off course as it learns."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Early Stopping:"})," Revisit the early stopping charm from the training section. Here, it serves as a powerful regularization potion, halting training before your model becomes a slave to noise."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Data Augmentation:"})," Recall the data augmentation magic from data preprocessing. It\u2019s not just about variety; it also serves as a subtle form of regularization that enhances your model\u2019s ability to generalize."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Regularization:"})," Utilize validation data to decide which regularization techniques to employ. Just as a wise oracle provides guidance, your validation set reveals the best path to controlling complexity."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"As you navigate the realm of regularization, think of yourself as a seasoned mage maintaining the delicate balance between power and control. Each technique you employ is a magical restraint, ensuring that your model\u2019s capabilities are channeled in the right direction. Just as a skilled magician can awe with a controlled display of magic, your model will captivate with its accuracy and generalization. \ud83c\udfa9\ud83d\udd2e"}),"\n",(0,t.jsx)(n.h2,{id:"choosing-the-right-loss-functions",children:"Choosing the Right Loss Functions"}),"\n",(0,t.jsx)(n.p,{children:"In the landscape of machine learning, loss functions are the compass that guides your model toward mastery. Just as a skilled navigator charts a course through uncharted waters, you\u2019ll choose the perfect loss function to steer your model towards accurate predictions:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Mean Squared Error:"})," Begin with the classic melody of mean squared error. This loss function quantifies the distance between predictions and actual values, guiding your model toward precision."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Cross-Entropy:"})," This loss function elegantly captures the divergence between predicted and actual probabilities, ensuring your model learns with finesse."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Huber Loss:"})," A blend of mean squared error and mean absolute error, it\u2019s ",(0,t.jsx)(n.em,{children:"robust against outliers"}),", helping your model navigate rough waters."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Custom Loss:"})," Compose your own loss function to address the unique nuances of your problem. Just as a composer tailors music to evoke specific emotions, you\u2019ll tailor your loss function to elicit accurate predictions."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Weighted Loss:"})," Tune the weights of your loss function to emphasize certain samples. This technique is like adjusting the volume of different instruments to achieve a balanced composition."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"As you explore the symphony of loss functions, envision yourself as a maestro directing your model\u2019s learning journey. Each loss function is a unique melody that guides your model through the intricacies of the data landscape. Just as a maestro extracts the best from each instrument, you\u2019ll coax your model to produce predictions that resonate with accuracy. \ud83c\udfb6\ud83c\udfa4"}),"\n",(0,t.jsx)(n.h2,{id:"crafting-custom-neural-architectures",children:"Crafting Custom Neural Architectures"}),"\n",(0,t.jsx)(n.p,{children:"As you venture deeper into the realm of machine learning, you\u2019ll realize that models are like sculptures waiting to take shape. Default architectures might not always fit your vision perfectly. It\u2019s time to don the mantle of a sculptor and craft custom neural architectures that bring your imagination to life:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"The Blank Canvas:"})," Begin with a clear mind. Analyze your problem\u2019s nuances, data characteristics, and objectives. This blank canvas is where your creative journey starts."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Architectural Elements:"})," Choose the building blocks for your masterpiece. From convolutional layers for images to recurrent layers for sequences, each element has a unique role in your design."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Skip Connections:"})," Embrace skip connections\u2014bridges that enable information to flow across different layers. Like secret passages in a castle, they enhance gradient flow and promote efficient learning."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Depth and Width:"})," Determine the depth and width of your architecture. Deeper networks capture intricate details, while wider networks handle complex relationships. Strike the right balance for your task."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Residual Networks:"})," Integrate residual networks (ResNets) for ",(0,t.jsx)(n.em,{children:"smoother gradient propagation"}),". These magical shortcuts encourage your model to focus on learning the residual information."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Attention:"})," Infuse attention mechanisms to allow your model to focus on relevant parts of the input. This technique is like a spotlight that illuminates the most important features."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Reinforcement Learning:"})," Dive into the exciting world of neural architecture search. Just as explorers discover new lands, you\u2019ll use reinforcement learning to unearth optimal architectures."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"Imagine yourself as an architect designing a grand structure. Your neural architecture is the blueprint, each layer a carefully chosen element contributing to the final form. Just as an architect molds space to evoke emotions, you\u2019ll shape your model to extract insights from data and make predictions that resonate with accuracy. \ud83c\udfdb\ufe0f\ud83d\udd25"}),"\n",(0,t.jsx)(n.h2,{id:"the-power-of-combining-models",children:"The Power of Combining Models"}),"\n",(0,t.jsx)(n.p,{children:"In the enchanted forest of machine learning, individual models are like trees\u2014strong, but together, they form a majestic forest. Ensembles are your magical incantations, summoning the power of multiple models to create a force that\u2019s greater than the sum of its parts:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Bagging:"})," Bootstrap Aggregating combines predictions from diverse models, smoothing out errors and enhancing generalization."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Boosting:"})," Algorithms like ",(0,t.jsx)(n.a,{href:"https://en.wikipedia.org/wiki/AdaBoost",children:"AdaBoost"})," and ",(0,t.jsx)(n.a,{href:"https://en.wikipedia.org/wiki/Gradient_boosting",children:"Gradient Boosting"})," mold weak learners into a formidable force, focusing on areas where previous models stumbled."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Stacking:"})," Stacking combines predictions from various models, creating a meta-model that learns to weigh their expertise."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Voting Ensemble:"})," Gather your models for a voting ensemble. Each model casts a vote, and the most popular prediction emerges victorious. It\u2019s like the collective wisdom of a council."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Ensemble Hyperparameters:"})," Tune the hyperparameters of your ensemble."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Ensemble Diversity:"})," Embrace diversity among your models. Different architectures, algorithms, and training strategies create a symphony of perspectives that boost ensemble performance."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"Ensemble techniques are your orchestra, playing in harmony to create predictions that surpass the capabilities of any individual model. Envision yourself as a conductor orchestrating a magnificent performance, where each model contributes its unique melody to create a harmonious ensemble. Just as an ensemble elevates a performance to new heights, your ensemble of models will elevate your predictions to levels of unparalleled accuracy. \ud83c\udfbb\ud83c\udfb9"}),"\n",(0,t.jsx)(n.h2,{id:"leveraging-callbacks-for-training-insights",children:"Leveraging Callbacks for Training Insights"}),"\n",(0,t.jsx)(n.p,{children:"In the labyrinth of machine learning, every training iteration is a step towards mastery. Callbacks are the lanterns that illuminate this path, providing insights and guidance throughout the training journey. Imagine yourself as an explorer equipped with these magical tools, unraveling the secrets of your model\u2019s learning process:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Early Stopping:"})," Summon the early stopping whisperer, a callback that listens to your model\u2019s performance. It knows when to ",(0,t.jsx)(n.em,{children:"halt training before overfitting"})," casts its shadow."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Learning Rate Scheduler:"})," Cast a spell to adjust the learning rate as your model learns. This dynamic tuning prevents overshooting and helps converge to the optimal point."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Model Checkpoint:"})," Enchant your training loop with a model checkpoint charm. It saves the model\u2019s progress at intervals, ensuring you never lose the map of your journey."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Custom Callback:"})," Craft your own custom callbacks to fit your unique needs. Whether it\u2019s monitoring specific metrics or injecting special techniques, these callbacks are your tailored assistants."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"As you explore the realm of callbacks, imagine yourself as a wise sage, attuned to the whispers of your model\u2019s learning journey. Each callback is a guide that imparts insights, helping you make informed decisions at every turn. Just as a sage interprets the signs of nature, you\u2019ll interpret the callbacks\u2019 cues to steer your model towards excellence. \ud83d\udd6f\ufe0f\ud83d\udd0d"}),"\n",(0,t.jsx)(n.h2,{id:"staying-updated-with-research-and-trends",children:"Staying Updated with Research and Trends"}),"\n",(0,t.jsx)(n.p,{children:"In the ever-evolving realm of machine learning, staying stagnant is not an option. Trends shift, algorithms evolve, and new techniques emerge like shooting stars across the night sky. You are the guardian of your craft, and mastering momentum is your key to staying at the forefront:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Lifelong Learning:"})," Take a lifelong learning oath. Just as a master craftsman hones their skills over time, commit to a journey of continuous improvement in this dynamic field."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Research Paper:"})," Embark on a quest to unravel research papers. Dive into the wealth of knowledge shared by the community, absorbing the latest breakthroughs and techniques."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Online Courses:"})," Enroll in online courses to refine your skills. These are your magical academies, offering structured lessons to help you master new technologies and methods."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Tech Conference:"})," Attend tech conferences and symposiums. Like a traveler exploring distant lands, immerse yourself in the sea of ideas, networking, and hands-on experience."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Blogosphere:"})," Contribute to the blogosphere by sharing your own insights and experiences. Just as a bard shares stories, you\u2019ll contribute to the collective knowledge of the community."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Collaboration:"})," Collaborate with fellow wizards\u2014engage in discussions, exchange ideas, and collaborate on projects. The synergy of minds is your catalyst for growth."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Model Zoo Exploration:"})," Explore pre-trained models and libraries. These treasure troves of pre-built models and functions are like enchanted artifacts that save time and effort."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"Imagine yourself as a celestial navigator, steering your ship through the cosmic sea of knowledge. Each trend, breakthrough, and technique is a star guiding your way. Just as a navigator charts new territories, you\u2019ll explore the uncharted horizons of machine learning, always seeking to harness the latest innovations and propel your craft forward. \ud83d\ude80\u2728"})]})}function d(e={}){const{wrapper:n}={...(0,r.a)(),...e.components};return n?(0,t.jsx)(n,{...e,children:(0,t.jsx)(h,{...e})}):h(e)}},11151:(e,n,i)=>{i.d(n,{Z:()=>o,a:()=>s});var t=i(67294);const r={},a=t.createContext(r);function s(e){const n=t.useContext(a);return t.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function o(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(r):e.components||r:s(e.components),t.createElement(a.Provider,{value:n},e.children)}}}]);
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+"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[7399],{98762:(e,n,i)=>{i.r(n),i.d(n,{assets:()=>l,contentTitle:()=>s,default:()=>d,frontMatter:()=>a,metadata:()=>o,toc:()=>c});var t=i(85893),r=i(11151);const a={title:"Advanced Guidelines for ML Model Training",authors:["mori"],tags:["Machine Learning","Model Training","Guidelines","Data Science","Neural Networks"]},s=void 0,o={permalink:"/portfolio/blog/guideline-ml",source:"@site/blog/guideline-ml.md",title:"Advanced Guidelines for ML Model Training",description:"Understanding Model Training",date:"2024-04-09T10:32:08.000Z",formattedDate:"April 9, 2024",tags:[{label:"Machine Learning",permalink:"/portfolio/blog/tags/machine-learning"},{label:"Model Training",permalink:"/portfolio/blog/tags/model-training"},{label:"Guidelines",permalink:"/portfolio/blog/tags/guidelines"},{label:"Data Science",permalink:"/portfolio/blog/tags/data-science"},{label:"Neural Networks",permalink:"/portfolio/blog/tags/neural-networks"}],readingTime:12.375,hasTruncateMarker:!0,authors:[{name:"Morteza Hosseini",title:"Data scientist / ML engineer",key:"mori"}],frontMatter:{title:"Advanced Guidelines for ML Model Training",authors:["mori"],tags:["Machine Learning","Model Training","Guidelines","Data Science","Neural Networks"]},unlisted:!1,prevItem:{title:"Get-Rich-Quick Schemes are a Bad Idea",permalink:"/portfolio/blog/get-rich-quick"},nextItem:{title:"Introducing Keras Core",permalink:"/portfolio/blog/keras-core"}},l={authorsImageUrls:[void 0]},c=[{value:"Understanding Model Training",id:"understanding-model-training",level:2},{value:"Preparing your data",id:"preparing-your-data",level:2},{value:"Writing Efficient Training Loops for Neural Networks",id:"writing-efficient-training-loops-for-neural-networks",level:2},{value:"Optimizing Hyperparameters",id:"optimizing-hyperparameters",level:2},{value:"Regularization Techniques",id:"regularization-techniques",level:2},{value:"Choosing the Right Loss Functions",id:"choosing-the-right-loss-functions",level:2},{value:"Crafting Custom Neural Architectures",id:"crafting-custom-neural-architectures",level:2},{value:"The Power of Combining Models",id:"the-power-of-combining-models",level:2},{value:"Leveraging Callbacks for Training Insights",id:"leveraging-callbacks-for-training-insights",level:2},{value:"Staying Updated with Research and Trends",id:"staying-updated-with-research-and-trends",level:2}];function h(e){const n={a:"a",em:"em",h2:"h2",li:"li",p:"p",strong:"strong",ul:"ul",...(0,r.a)(),...e.components};return(0,t.jsxs)(t.Fragment,{children:[(0,t.jsx)(n.h2,{id:"understanding-model-training",children:"Understanding Model Training"}),"\n",(0,t.jsx)(n.p,{children:"Welcome to the captivating realm of machine learning, where algorithms breathe life into data and unveil patterns that were once hidden in the shadows. Before we dive into the intricate dance of code and data, let\u2019s take a moment to understand the essence of model training."}),"\n",(0,t.jsx)(n.p,{children:"Imagine yourself as an artisan, crafting a masterpiece from raw materials. Just as a painter starts with a blank canvas, you begin with a dataset rich in information. This dataset is your palette, and your model is the brush that will paint the future. \ud83c\udfa8\ud83e\udd16"}),"\n",(0,t.jsx)(n.p,{children:"Model training is the process of imbuing your creation with the ability to learn from data and make predictions. Just as a symphony conductor guides each musician to play in harmony, you guide your model through the data."}),"\n",(0,t.jsx)(n.h2,{id:"preparing-your-data",children:"Preparing your data"}),"\n",(0,t.jsx)(n.p,{children:"In the grand symphony of model training, data preprocessing plays the role of tuning each instrument before the performance. Just as a musician wouldn\u2019t play with a broken string, your model won\u2019t perform well with unrefined data. \ud83c\udfbb\ud83c\udfbc"}),"\n",(0,t.jsx)(n.p,{children:"Let\u2019s embark on the journey of transforming raw data into a harmonious dataset:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Data Cleaning:"})," Begin by scrubbing your data clean. Detect and handle ",(0,t.jsx)(n.em,{children:"missing values"}),", ",(0,t.jsx)(n.em,{children:"outliers"}),", and inconsistencies that could disrupt your model\u2019s rhythm."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Feature Engineering:"})," Craft a melody of features that resonate with your model\u2019s ",(0,t.jsx)(n.em,{children:"objective"}),". Engineer meaningful features that capture the essence of your problem domain."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Scaling and Normalization:"})," Ensure that your features are on the same scale, allowing your model to converge faster and avoid undue influence from dominant features."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Encoding:"})," Convert ",(0,t.jsx)(n.em,{children:"categorical"})," variables into a format that the model can understand. Whether it\u2019s ",(0,t.jsx)(n.em,{children:"one-hot encoding"}),", ",(0,t.jsx)(n.em,{children:"label encoding"}),", or other techniques, create a harmonious blend of categorical and numerical features."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Train-Validation-Test Split:"})," Divide your dataset into training, validation, and test sets. Each has a unique role in refining your model\u2019s performance."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Data Augmentation:"})," For image and sequence data, explore the realm of data augmentation. This technique introduces variety into your training data, enabling your model to ",(0,t.jsx)(n.em,{children:"generalize better"}),"."]}),"\n"]}),"\n",(0,t.jsx)(n.h2,{id:"writing-efficient-training-loops-for-neural-networks",children:"Writing Efficient Training Loops for Neural Networks"}),"\n",(0,t.jsx)(n.p,{children:"Now that your dataset is preprocessed, it\u2019s time to step into the realm of code and write the enchanting spells that will bring your model to life. Just as a playwright crafts a compelling script, you\u2019ll code a training loop that guides your model\u2019s evolution:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Initialization:"})," Set the stage by initializing your model\u2019s architecture, loss function, and optimizer. Think of this as creating the canvas upon which your model will paint its predictions."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Epoch:"})," Enter the world of epochs, where each iteration weaves a new chapter of learning. Design your training loop to iterate through your dataset, presenting patterns to your model that it can decipher."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Batch:"})," Instead of presenting your model with the entire dataset at once, serve it bite-sized portions called batches. This ",(0,t.jsx)(n.em,{children:"accelerates learning"})," and ",(0,t.jsx)(n.em,{children:"reduces memory consumption"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Backpropagation:"})," In this step, your model learns from its mistakes by adjusting its internal parameters. Gradient descent guides it closer to accurate predictions with each step."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Validation:"})," Don\u2019t forget your validation set! After each epoch, serenade your model with the validation data to gauge its performance and ",(0,t.jsx)(n.em,{children:"prevent overfitting"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Early Stopping:"})," A magical trick to avoid prolonged training. If your model\u2019s performance on the validation set stagnates or worsens, elegantly halt the training and ",(0,t.jsx)(n.em,{children:"save resources"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Checkpoint:"})," Cast a checkpoint spell to save your model\u2019s progress during training. If anything goes awry, you can always ",(0,t.jsx)(n.em,{children:"pick up where you left off"}),"."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"As you craft your training loop, envision yourself as a conductor guiding your model towards mastery. Each line of code is a note in the symphony of learning, harmonizing to create a melody of intelligence. Just as a conductor brings out the best from each musician, you\u2019ll coax out the finest predictions from your model, one iteration at a time. \ud83c\udfb5\ud83d\udd25"}),"\n",(0,t.jsx)(n.h2,{id:"optimizing-hyperparameters",children:"Optimizing Hyperparameters"}),"\n",(0,t.jsx)(n.p,{children:"As you embark on the journey of machine learning, you\u2019ll encounter hyperparameters\u2014settings that wield immense influence over your model\u2019s performance. Much like a blacksmith forging a mighty sword, you\u2019ll shape these hyperparameters to enhance your model\u2019s power:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Hyperspace:"})," Dive into the realm of hyperparameters\u2014learning rate, batch size, hidden units, and more. Each choice alters your model\u2019s trajectory, and finding the right balance is your quest."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Grid Search:"})," Equip yourself with a grid search spell to systematically explore different hyperparameter combinations. Uncover the sweet spot that ",(0,t.jsx)(n.em,{children:"maximizes"})," your model\u2019s performance."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Random Search:"})," Embark on an adventure that balances exploration and efficiency. Randomly sample hyperparameter values to discover hidden gems without exhaustive searching."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Bayesian Optimization:"})," Unveil the elegance of Bayesian optimization\u2014a smarter way to search for optimal hyperparameters. This method intelligently ",(0,t.jsx)(n.em,{children:"narrows down the search space"})," and accelerates your quest."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Regularization:"})," Beware the curse of overfitting on your hyperparameters. Just as knights wear armor for protection, regularize your hyperparameters to prevent them from ",(0,t.jsx)(n.em,{children:"fitting noise"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Automated Tuning:"})," Employ automated hyperparameter tuning libraries like ",(0,t.jsx)(n.a,{href:"https://optuna.org/",children:"Optuna"})," or ",(0,t.jsx)(n.a,{href:"https://hyperopt.github.io/hyperopt/",children:"Hyperopt"}),". These tools streamline the search for optimal hyperparameters, freeing you to focus on your model\u2019s magic."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"In your pursuit of optimal hyperparameters, imagine yourself as an alchemist blending rare ingredients. Each hyperparameter value is a crucial component, and the right blend will transform your model from good to extraordinary. Just as an alchemist\u2019s concoction holds secrets, your choice of hyperparameters will unlock the hidden potential within your model. \ud83e\uddea\u2728"}),"\n",(0,t.jsx)(n.h2,{id:"regularization-techniques",children:"Regularization Techniques"}),"\n",(0,t.jsxs)(n.p,{children:["In the intricate tapestry of machine learning, complexity can sometimes lead to chaos. Regularization techniques are your spells to tame the unruly, ensuring that your model doesn\u2019t succumb to ",(0,t.jsx)(n.em,{children:"overfitting"}),". Just as a skilled trainer guides a wild beast, you\u2019ll control the complexity of your model:"]}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Lasso and Ridge:"})," Explore the wonders of L1 and L2 regularization. These techniques add a touch of magic to your loss function, ",(0,t.jsx)(n.em,{children:"penalizing overly complex models"})," and ushering in simplicity."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Dropout:"})," Unveil the enigmatic power of dropout\u2014a technique where neurons \u201cdisappear\u201d during training. This prevents ",(0,t.jsx)(n.em,{children:"co-dependency"})," and encourages each neuron to be self-sufficient."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Batch Normalization:"})," Cast a charm that stabilizes learning by normalizing the inputs of each layer. This technique keeps your model from wandering off course as it learns."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Early Stopping:"})," Revisit the early stopping charm from the training section. Here, it serves as a powerful regularization potion, halting training before your model becomes a slave to noise."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Data Augmentation:"})," Recall the data augmentation magic from data preprocessing. It\u2019s not just about variety; it also serves as a subtle form of regularization that enhances your model\u2019s ability to generalize."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Regularization:"})," Utilize validation data to decide which regularization techniques to employ. Just as a wise oracle provides guidance, your validation set reveals the best path to controlling complexity."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"As you navigate the realm of regularization, think of yourself as a seasoned mage maintaining the delicate balance between power and control. Each technique you employ is a magical restraint, ensuring that your model\u2019s capabilities are channeled in the right direction. Just as a skilled magician can awe with a controlled display of magic, your model will captivate with its accuracy and generalization. \ud83c\udfa9\ud83d\udd2e"}),"\n",(0,t.jsx)(n.h2,{id:"choosing-the-right-loss-functions",children:"Choosing the Right Loss Functions"}),"\n",(0,t.jsx)(n.p,{children:"In the landscape of machine learning, loss functions are the compass that guides your model toward mastery. Just as a skilled navigator charts a course through uncharted waters, you\u2019ll choose the perfect loss function to steer your model towards accurate predictions:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Mean Squared Error:"})," Begin with the classic melody of mean squared error. This loss function quantifies the distance between predictions and actual values, guiding your model toward precision."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Cross-Entropy:"})," This loss function elegantly captures the divergence between predicted and actual probabilities, ensuring your model learns with finesse."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Huber Loss:"})," A blend of mean squared error and mean absolute error, it\u2019s ",(0,t.jsx)(n.em,{children:"robust against outliers"}),", helping your model navigate rough waters."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Custom Loss:"})," Compose your own loss function to address the unique nuances of your problem. Just as a composer tailors music to evoke specific emotions, you\u2019ll tailor your loss function to elicit accurate predictions."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Weighted Loss:"})," Tune the weights of your loss function to emphasize certain samples. This technique is like adjusting the volume of different instruments to achieve a balanced composition."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"As you explore the symphony of loss functions, envision yourself as a maestro directing your model\u2019s learning journey. Each loss function is a unique melody that guides your model through the intricacies of the data landscape. Just as a maestro extracts the best from each instrument, you\u2019ll coax your model to produce predictions that resonate with accuracy. \ud83c\udfb6\ud83c\udfa4"}),"\n",(0,t.jsx)(n.h2,{id:"crafting-custom-neural-architectures",children:"Crafting Custom Neural Architectures"}),"\n",(0,t.jsx)(n.p,{children:"As you venture deeper into the realm of machine learning, you\u2019ll realize that models are like sculptures waiting to take shape. Default architectures might not always fit your vision perfectly. It\u2019s time to don the mantle of a sculptor and craft custom neural architectures that bring your imagination to life:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"The Blank Canvas:"})," Begin with a clear mind. Analyze your problem\u2019s nuances, data characteristics, and objectives. This blank canvas is where your creative journey starts."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Architectural Elements:"})," Choose the building blocks for your masterpiece. From convolutional layers for images to recurrent layers for sequences, each element has a unique role in your design."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Skip Connections:"})," Embrace skip connections\u2014bridges that enable information to flow across different layers. Like secret passages in a castle, they enhance gradient flow and promote efficient learning."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Depth and Width:"})," Determine the depth and width of your architecture. Deeper networks capture intricate details, while wider networks handle complex relationships. Strike the right balance for your task."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Residual Networks:"})," Integrate residual networks (ResNets) for ",(0,t.jsx)(n.em,{children:"smoother gradient propagation"}),". These magical shortcuts encourage your model to focus on learning the residual information."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Attention:"})," Infuse attention mechanisms to allow your model to focus on relevant parts of the input. This technique is like a spotlight that illuminates the most important features."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Reinforcement Learning:"})," Dive into the exciting world of neural architecture search. Just as explorers discover new lands, you\u2019ll use reinforcement learning to unearth optimal architectures."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"Imagine yourself as an architect designing a grand structure. Your neural architecture is the blueprint, each layer a carefully chosen element contributing to the final form. Just as an architect molds space to evoke emotions, you\u2019ll shape your model to extract insights from data and make predictions that resonate with accuracy. \ud83c\udfdb\ufe0f\ud83d\udd25"}),"\n",(0,t.jsx)(n.h2,{id:"the-power-of-combining-models",children:"The Power of Combining Models"}),"\n",(0,t.jsx)(n.p,{children:"In the enchanted forest of machine learning, individual models are like trees\u2014strong, but together, they form a majestic forest. Ensembles are your magical incantations, summoning the power of multiple models to create a force that\u2019s greater than the sum of its parts:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Bagging:"})," Bootstrap Aggregating combines predictions from diverse models, smoothing out errors and enhancing generalization."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Boosting:"})," Algorithms like ",(0,t.jsx)(n.a,{href:"https://en.wikipedia.org/wiki/AdaBoost",children:"AdaBoost"})," and ",(0,t.jsx)(n.a,{href:"https://en.wikipedia.org/wiki/Gradient_boosting",children:"Gradient Boosting"})," mold weak learners into a formidable force, focusing on areas where previous models stumbled."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Stacking:"})," Stacking combines predictions from various models, creating a meta-model that learns to weigh their expertise."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Voting Ensemble:"})," Gather your models for a voting ensemble. Each model casts a vote, and the most popular prediction emerges victorious. It\u2019s like the collective wisdom of a council."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Ensemble Hyperparameters:"})," Tune the hyperparameters of your ensemble."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Ensemble Diversity:"})," Embrace diversity among your models. Different architectures, algorithms, and training strategies create a symphony of perspectives that boost ensemble performance."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"Ensemble techniques are your orchestra, playing in harmony to create predictions that surpass the capabilities of any individual model. Envision yourself as a conductor orchestrating a magnificent performance, where each model contributes its unique melody to create a harmonious ensemble. Just as an ensemble elevates a performance to new heights, your ensemble of models will elevate your predictions to levels of unparalleled accuracy. \ud83c\udfbb\ud83c\udfb9"}),"\n",(0,t.jsx)(n.h2,{id:"leveraging-callbacks-for-training-insights",children:"Leveraging Callbacks for Training Insights"}),"\n",(0,t.jsx)(n.p,{children:"In the labyrinth of machine learning, every training iteration is a step towards mastery. Callbacks are the lanterns that illuminate this path, providing insights and guidance throughout the training journey. Imagine yourself as an explorer equipped with these magical tools, unraveling the secrets of your model\u2019s learning process:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Early Stopping:"})," Summon the early stopping whisperer, a callback that listens to your model\u2019s performance. It knows when to ",(0,t.jsx)(n.em,{children:"halt training before overfitting"})," casts its shadow."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Learning Rate Scheduler:"})," Cast a spell to adjust the learning rate as your model learns. This dynamic tuning prevents overshooting and helps converge to the optimal point."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Model Checkpoint:"})," Enchant your training loop with a model checkpoint charm. It saves the model\u2019s progress at intervals, ensuring you never lose the map of your journey."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Custom Callback:"})," Craft your own custom callbacks to fit your unique needs. Whether it\u2019s monitoring specific metrics or injecting special techniques, these callbacks are your tailored assistants."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"As you explore the realm of callbacks, imagine yourself as a wise sage, attuned to the whispers of your model\u2019s learning journey. Each callback is a guide that imparts insights, helping you make informed decisions at every turn. Just as a sage interprets the signs of nature, you\u2019ll interpret the callbacks\u2019 cues to steer your model towards excellence. \ud83d\udd6f\ufe0f\ud83d\udd0d"}),"\n",(0,t.jsx)(n.h2,{id:"staying-updated-with-research-and-trends",children:"Staying Updated with Research and Trends"}),"\n",(0,t.jsx)(n.p,{children:"In the ever-evolving realm of machine learning, staying stagnant is not an option. Trends shift, algorithms evolve, and new techniques emerge like shooting stars across the night sky. You are the guardian of your craft, and mastering momentum is your key to staying at the forefront:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Lifelong Learning:"})," Take a lifelong learning oath. Just as a master craftsman hones their skills over time, commit to a journey of continuous improvement in this dynamic field."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Research Paper:"})," Embark on a quest to unravel research papers. Dive into the wealth of knowledge shared by the community, absorbing the latest breakthroughs and techniques."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Online Courses:"})," Enroll in online courses to refine your skills. These are your magical academies, offering structured lessons to help you master new technologies and methods."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Tech Conference:"})," Attend tech conferences and symposiums. Like a traveler exploring distant lands, immerse yourself in the sea of ideas, networking, and hands-on experience."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Blogosphere:"})," Contribute to the blogosphere by sharing your own insights and experiences. Just as a bard shares stories, you\u2019ll contribute to the collective knowledge of the community."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Collaboration:"})," Collaborate with fellow wizards\u2014engage in discussions, exchange ideas, and collaborate on projects. The synergy of minds is your catalyst for growth."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Model Zoo Exploration:"})," Explore pre-trained models and libraries. These treasure troves of pre-built models and functions are like enchanted artifacts that save time and effort."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"Imagine yourself as a celestial navigator, steering your ship through the cosmic sea of knowledge. Each trend, breakthrough, and technique is a star guiding your way. Just as a navigator charts new territories, you\u2019ll explore the uncharted horizons of machine learning, always seeking to harness the latest innovations and propel your craft forward. \ud83d\ude80\u2728"})]})}function d(e={}){const{wrapper:n}={...(0,r.a)(),...e.components};return n?(0,t.jsx)(n,{...e,children:(0,t.jsx)(h,{...e})}):h(e)}},11151:(e,n,i)=>{i.d(n,{Z:()=>o,a:()=>s});var t=i(67294);const r={},a=t.createContext(r);function s(e){const n=t.useContext(a);return t.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function o(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(r):e.components||r:s(e.components),t.createElement(a.Provider,{value:n},e.children)}}}]);
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Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there\u2019s no shortcut to success in data science."}),"\n",(0,t.jsx)(a.p,{children:"Let\u2019s face it, we all want to be successful and financially stable. And with the booming field of data science and machine learning, it\u2019s tempting to believe that we can achieve our financial dreams by simply learning a few skills and jumping on the bandwagon. Unfortunately, the reality is far from that."}),"\n",(0,t.jsx)(a.p,{children:"Here, we\u2019ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let\u2019s debunk some myths!"}),"\n",(0,t.jsx)(a.h2,{id:"myth-1-you-can-become-a-data-science-expert-in-a-few-weeks",children:"Myth #1: You can become a data science expert in a few weeks"}),"\n",(0,t.jsx)(a.p,{children:"One of the most common promises of get-rich-quick schemes is that you can become a \u201cdata science expert\u201d in just a few weeks or months. They claim that their courses or bootcamps can teach you everything you need to know to land a high-paying job or start your own business."}),"\n",(0,t.jsx)(a.p,{children:"The reality is that data science is a complex and multidisciplinary field that requires years of study, practice, and experimentation to master. 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Moreover, big data can pose ethical and privacy challenges, such as data ownership, consent, and security, that require careful consideration and management."}),"\n",(0,t.jsx)(a.p,{children:"Therefore, it\u2019s important to choose the appropriate data size and quality for the problem at hand, and to balance the trade-offs between data quantity and data quality, as well as the cost and benefit of collecting and processing more data."}),"\n",(0,t.jsx)(a.h2,{id:"myth-7-deep-learning-is-the-only-type-of-machine-learning",children:"Myth #7: Deep learning is the only type of machine learning"}),"\n",(0,t.jsx)(a.p,{children:"Deep learning, a type of machine learning that involves neural networks with many layers, has gained a lot of attention and success in recent years, especially in domains such as image processing, speech recognition, and natural language processing. However, some people believe that deep learning is the only type of machine learning and that it\u2019s the ultimate solution to all machine learning problems."}),"\n",(0,t.jsx)(a.p,{children:"The reality is that deep learning is only one type of machine learning, and it\u2019s not always the most suitable or efficient one. Other types of machine learning, such as random forests, support vector machines, and logistic regression, have their own strengths and weaknesses and can be more appropriate for different types of problems and data."}),"\n",(0,t.jsx)(a.p,{children:"Moreover, deep learning requires a lot of data, computation, and expertise to train and tune, and it can suffer from overfitting, generalization, and interpretability issues. 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So, stay tuned, and let\u2019s debunk some myths!"}),"\n",(0,t.jsx)(a.h2,{id:"myth-1-you-can-become-a-data-science-expert-in-a-few-weeks",children:"Myth #1: You can become a data science expert in a few weeks"}),"\n",(0,t.jsx)(a.p,{children:"One of the most common promises of get-rich-quick schemes is that you can become a \u201cdata science expert\u201d in just a few weeks or months. They claim that their courses or bootcamps can teach you everything you need to know to land a high-paying job or start your own business."}),"\n",(0,t.jsx)(a.p,{children:"The reality is that data science is a complex and multidisciplinary field that requires years of study, practice, and experimentation to master. It involves not only technical skills like programming and machine learning but also soft skills like communication, critical thinking, and problem-solving."}),"\n",(0,t.jsx)(a.p,{children:"Moreover, data science is constantly evolving, with new tools, techniques, and applications emerging every day. So, even if you manage to learn the basics of data science in a short period, you\u2019ll still need to keep learning and updating your skills throughout your career."}),"\n",(0,t.jsx)(a.h2,{id:"myth-2-you-can-make-a-lot-of-money-quickly-with-data-science",children:"Myth #2: You can make a lot of money quickly with data science"}),"\n",(0,t.jsx)(a.p,{children:"Another myth is that you can make a lot of money quickly by working as a data scientist, consultant, or entrepreneur. They showcase success stories of people who have earned six or seven-figure salaries or built multi-million-dollar businesses in a short time."}),"\n",(0,t.jsx)(a.p,{children:"The reality is that while data science can be a lucrative field, it\u2019s not a guarantee of wealth and success. Your earning potential depends on many factors, such as your skills, experience, location, industry, and demand. Moreover, there\u2019s a lot of competition in the data science job market, with many qualified candidates vying for the same positions."}),"\n",(0,t.jsx)(a.p,{children:"In addition, starting a data science business is not easy, and it requires a lot of planning, investment, and risk-taking. You\u2019ll need to have a solid business plan, a clear value proposition, a strong team, and a scalable product or service. 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Statistics, linear algebra, and calculus are the building blocks of data science, and without a solid understanding of them, you\u2019ll struggle to solve real-world problems and communicate your findings effectively."}),"\n",(0,t.jsx)(a.p,{children:"Moreover, knowing how to use a specific tool or library is not enough to be a successful data scientist. You need to be able to choose the right tool for the job, understand its strengths and limitations, and be able to customize it to fit your specific needs. That requires a deep understanding of the underlying concepts and principles of data science."}),"\n",(0,t.jsx)(a.h2,{id:"myth-4-you-can-rely-solely-on-online-courses-and-tutorials",children:"Myth #4: You can rely solely on online courses and tutorials"}),"\n",(0,t.jsx)(a.p,{children:"The next promise is that you can rely solely on online courses and tutorials to learn everything you need to know about data science. They claim that you don\u2019t need to attend a traditional university or obtain a degree or certification to become a successful data scientist."}),"\n",(0,t.jsx)(a.p,{children:"The reality is that while online courses and tutorials can be a valuable source of information and inspiration, they\u2019re not a substitute for formal education or hands-on experience. Data science is a complex and dynamic field that requires a deep and broad understanding of multiple disciplines, including mathematics, statistics, computer science, and domain expertise."}),"\n",(0,t.jsx)(a.p,{children:"Moreover, online courses and tutorials are often limited in scope and quality, and they can\u2019t provide you with the same level of feedback, mentorship, and networking opportunities as a traditional university or professional program. They also can\u2019t expose you to the real-world challenges and complexities of data science projects, which often involve messy, incomplete, and unstructured data, conflicting goals and priorities, and ethical and legal considerations."}),"\n",(0,t.jsx)(a.p,{children:"Therefore, if you\u2019re serious about pursuing a career in data science, it\u2019s recommended to obtain a degree or certification from a reputable institution or professional organization. This will not only provide you with a solid foundation in data science but also enhance your credibility, employability, and earning potential."}),"\n",(0,t.jsx)(a.h2,{id:"myth-5-you-can-automate-everything-with-machine-learning",children:"Myth #5: You can automate everything with machine learning"}),"\n",(0,t.jsx)(a.p,{children:"One of the biggest myths perpetuated by some proponents of machine learning is that you can automate everything with it. They claim that machine learning can replace human experts and decision-makers in various domains, such as healthcare, finance, and transportation, and make better and faster decisions based on data."}),"\n",(0,t.jsx)(a.p,{children:"The reality is that while machine learning can automate some tasks and improve some decisions, it cannot replace human judgment and ethical considerations. Machine learning models are only as good as the data they\u2019re trained on and the assumptions they make, and they can suffer from biases, errors, and uncertainties that can lead to incorrect or unfair decisions."}),"\n",(0,t.jsx)(a.p,{children:"Moreover, machine learning models can\u2019t explain how they arrived at their decisions or provide context and nuance that human experts can. Therefore, it\u2019s important to use them as a complement to human expertise and judgment, and to ensure that the models are transparent, explainable, and aligned with ethical and social values."}),"\n",(0,t.jsx)(a.h2,{id:"myth-6-big-data-is-always-better-than-small-data",children:"Myth #6: Big data is always better than small data"}),"\n",(0,t.jsx)(a.p,{children:"Another myth perpetuated by some proponents of data science is that big data is always better than small data. They claim that more data leads to more accurate and reliable models and insights, and that small data is insufficient and irrelevant for modern data science."}),"\n",(0,t.jsx)(a.p,{children:"The reality is that while big data can provide more samples and variety of data, it can also introduce noise, redundancy, and complexity that can make analysis and modeling more difficult and less interpretable. Moreover, big data can pose ethical and privacy challenges, such as data ownership, consent, and security, that require careful consideration and management."}),"\n",(0,t.jsx)(a.p,{children:"Therefore, it\u2019s important to choose the appropriate data size and quality for the problem at hand, and to balance the trade-offs between data quantity and data quality, as well as the cost and benefit of collecting and processing more data."}),"\n",(0,t.jsx)(a.h2,{id:"myth-7-deep-learning-is-the-only-type-of-machine-learning",children:"Myth #7: Deep learning is the only type of machine learning"}),"\n",(0,t.jsx)(a.p,{children:"Deep learning, a type of machine learning that involves neural networks with many layers, has gained a lot of attention and success in recent years, especially in domains such as image processing, speech recognition, and natural language processing. However, some people believe that deep learning is the only type of machine learning and that it\u2019s the ultimate solution to all machine learning problems."}),"\n",(0,t.jsx)(a.p,{children:"The reality is that deep learning is only one type of machine learning, and it\u2019s not always the most suitable or efficient one. Other types of machine learning, such as random forests, support vector machines, and logistic regression, have their own strengths and weaknesses and can be more appropriate for different types of problems and data."}),"\n",(0,t.jsx)(a.p,{children:"Moreover, deep learning requires a lot of data, computation, and expertise to train and tune, and it can suffer from overfitting, generalization, and interpretability issues. So, it\u2019s important to choose the appropriate machine learning algorithm and architecture based on the problem at hand, the data characteristics, and the available resources and expertise."}),"\n",(0,t.jsx)(a.h2,{id:"conclusion",children:"Conclusion"}),"\n",(0,t.jsx)(a.p,{children:"Get-rich-quick schemes in data science are based on false promises, misleading claims, and dangerous myths. They exploit people\u2019s aspirations and fears, and they undermine the integrity and credibility of data science as a profession and a discipline. 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-"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[3406],{8063:e=>{e.exports=JSON.parse('{"blogPosts":[{"id":"/anonym-pseudonym","metadata":{"permalink":"/portfolio/blog/anonym-pseudonym","source":"@site/blog/anonym-pseudonym.md","title":"Anonymization vs. Pseudonymization","description":"The Yin and Yang of Data Privacy","date":"2024-04-05T14:49:00.000Z","formattedDate":"April 5, 2024","tags":[{"label":"Data Privacy","permalink":"/portfolio/blog/tags/data-privacy"},{"label":"Anonymization","permalink":"/portfolio/blog/tags/anonymization"},{"label":"Pseudonymization","permalink":"/portfolio/blog/tags/pseudonymization"},{"label":"Data Science","permalink":"/portfolio/blog/tags/data-science"},{"label":"Python","permalink":"/portfolio/blog/tags/python"}],"readingTime":7.91,"hasTruncateMarker":true,"authors":[{"name":"Morteza Hosseini","title":"Data scientist / ML engineer","key":"mori"}],"frontMatter":{"title":"Anonymization vs. Pseudonymization","authors":["mori"],"tags":["Data Privacy","Anonymization","Pseudonymization","Data Science","Python"]},"unlisted":false,"nextItem":{"title":"Exploring AI Ethics: 15 Key Q&As","permalink":"/portfolio/blog/ethics"}},"content":"## The Yin and Yang of Data Privacy\\n\\nIn the digital era, data is the new oil. It fuels innovation, drives decision-making, and propels businesses towards success. But with great power comes great responsibility. As data scientists, we must ensure that the data we handle is not misused or exploited, causing harm to the individuals it represents. Enter the twin superheroes of data protection: Anonymization and Pseudonymization.\\n\\nAnonymization and Pseudonymization are two essential techniques in the realm of data privacy. They serve as the yin and yang, balancing the need for data utility with the imperative of privacy protection. While they may seem similar at first glance, each has its distinct characteristics and use cases.\\n\\n\x3c!--truncate--\x3e\\n\\n**Anonymization** is the process of _irreversibly_ transforming data in such a way that a data subject can no longer be identified directly or indirectly. It\u2019s like a one-way ticket: once the data is anonymized, there\u2019s no going back. It\u2019s a powerful tool for protecting privacy, but it also means that certain analyses requiring linkage back to the original data are off the table.\\n\\nOn the other hand, **Pseudonymization** is a reversible process that replaces or obscures identifiable data elements within a data record with artificial identifiers or pseudonyms. It\u2019s a bit like a masquerade ball: the data subjects are concealed behind masks, but they can be re-identified if necessary. This makes pseudonymization a flexible tool that can maintain data\u2019s utility while still offering a level of privacy protection.\\n\\nIn this blog post, we\u2019ll dive deep into these two techniques, exploring their nuances, differences, and applications. So, buckle up and get ready for an exciting journey into the world of data privacy!\\n\\n## Unraveling the Mysteries of Anonymization\\n\\nAnonymization is a potent technique that can transform your data into a privacy-compliant asset. In the realm of data privacy, Anonymization refers to the process of removing or altering personally identifiable information from your data so that the individuals whom the data describe remain anonymous.\\n\\n### The Power of Anonymization\\n\\nImagine you\u2019re a data scientist working with a dataset chock-full of sensitive information. You need to share this data with your team, but you also need to ensure you\u2019re not violating any privacy laws or ethical guidelines. Anonymization comes to your rescue here. By anonymizing your data, you can share it freely without worrying about exposing personal information.\\n\\n### Python: The Data Scientist\u2019s Swiss Army Knife\\n\\nPython, with its vast array of libraries and tools, is the perfect companion for a data scientist looking to implement Anonymization. Let\u2019s take a look at a simple example using cryptographic hashing with the hashlib library.\\n\\n```python title=\\"Python\\"\\nimport hashlib\\nfrom pprint import pprint\\n\\n# Let\'s consider a simple data list\\ndata = [\\"Alice\\", \\"Bob\\", \\"Charlie\\", \\"Alice\\", \\"Bob\\", \\"Dave\\"]\\n\\n# Anonymize the data using SHA256 hashing\\nanonymized_data = [\\n    hashlib.sha256(name.encode()).hexdigest() for name in data\\n]\\n\\npprint(anonymized_data)\\n```\\n\\n```\\n[\'3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043\',\\n\'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961\',\\n\'6e81b1255ad51bb201a2b8afa9b66653297ae0217f833b14b39b5231228bf968\',\\n\'3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043\',\\n\'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961\',\\n\'809a721743350c0c49a7b444ad3aeaf1341fdd48d1bf510e08b008edab72dc70\']\\n```\\n\\nIn this example, we\u2019ve replaced each name in our data with a unique hash generated by the SHA256 algorithm. The original names can\u2019t be retrieved from these hashes, making the data effectively anonymized.\\n\\nHowever, this is a simple example. Real-world data can be much more complex and may require more sophisticated anonymization techniques. But not to worry! Python\u2019s vast ecosystem of libraries and tools has you covered no matter how complex your data might be.\\n\\n## The Art of Pseudonymization\\n\\nAfter our deep dive into anonymization, let\u2019s now turn our attention to its counterpart, Pseudonymization. In the grand tapestry of data privacy, Pseudonymization is a technique that replaces identifiable data with artificial identifiers or pseudonyms. Unlike anonymization, pseudonymization is reversible under controlled conditions, allowing data subjects to be re-identified when necessary.\\n\\n### The Flexibility of Pseudonymization\\n\\nImagine you\u2019re a healthcare researcher working on a longitudinal study. You need to track individual patients\u2019 health outcomes over time, but you also need to respect their privacy. Pseudonymization is your ally in this scenario. By replacing identifiable information like names or social security numbers with pseudonyms, you can protect your patients\u2019 privacy while still being able to track their data over time.\\n\\n### Python to the Rescue: Implementing Pseudonymization\\n\\nPython, with its diverse ecosystem of libraries and tools, makes implementing pseudonymization a breeze. Let\u2019s look at an example using Python\u2019s built-in `uuid` library to generate unique pseudonyms for our data.\\n\\n```python title=\\"Python\\"\\nimport uuid\\nfrom pprint import pprint\\n\\n# Let\'s consider a simple data list\\ndata = [\'Alice\', \'Bob\', \'Charlie\', \'Alice\', \'Bob\', \'Dave\']\\n\\n# Create a dictionary to store our pseudonyms\\npseudonyms = {}\\n\\n# Pseudonymize the data\\npseudonymized_data = [\\n    pseudonyms.setdefault(name, str(uuid.uuid4())) for name in data\\n]\\n\\npprint(pseudonymized_data)\\n```\\n\\n```\\n[\'dfaa2289-1611-44ea-950a-8c9b43544a68\',\\n\'f20c6a2f-bdc2-40b4-896a-614bec5baa77\',\\n\'9244f9fb-f21b-4111-b982-ce81f9374daf\',\\n\'dfaa2289-1611-44ea-950a-8c9b43544a68\',\\n\'f20c6a2f-bdc2-40b4-896a-614bec5baa77\',\\n\'a2697683-8e88-4b5b-add1-51cc4533ffda\']\\n```\\n\\nIn this example, we\u2019ve replaced each name in our data with a unique pseudonym generated by the `uuid` library. The pseudonyms are stored in a dictionary, allowing us to consistently replace each name with the same pseudonym across our data. However, with the dictionary, we can also reverse the pseudonymization process if needed, which is not possible with anonymization.\\n\\n```python title=\\"Python\\"\\npprint(pseudonyms)\\n```\\n\\n```\\n{\'Alice\': \'dfaa2289-1611-44ea-950a-8c9b43544a68\',\\n\'Bob\': \'f20c6a2f-bdc2-40b4-896a-614bec5baa77\',\\n\'Charlie\': \'9244f9fb-f21b-4111-b982-ce81f9374daf\',\\n\'Dave\': \'a2697683-8e88-4b5b-add1-51cc4533ffda\'}\\n```\\n\\n## Comparing Anonymization and Pseudonymization\\n\\nNow that we\u2019ve explored both anonymization and pseudonymization, let\u2019s take a step back and compare these two techniques. While they both aim to protect privacy, their differences in reversibility, data utility, and risk levels make them suitable for different scenarios.\\n\\n### Reversibility: A One-Way Street vs. A Two-Way Lane\\n\\nAnonymization is a one-way street. Once data is anonymized, it\u2019s impossible to revert it back to its original form. This makes anonymization a strong tool for privacy protection, as it eliminates the risk of re-identification.\\n\\nOn the contrary, pseudonymization is a two-way lane. It replaces identifiable data with pseudonyms, but this process can be reversed under controlled conditions. This allows for the possibility of re-identification, which can be beneficial in certain scenarios, such as longitudinal studies or customer relationship management.\\n\\n### Data Utility: A Trade-Off\\n\\nAnonymization provides a high level of privacy protection, but it comes at the cost of data utility. By irreversibly transforming data, anonymization can limit the types of analysis that can be performed. For example, it\u2019s impossible to conduct individual-level analysis or link data across multiple datasets once it\u2019s been anonymized.\\n\\nPseudonymization, on the other hand, maintains a higher level of data utility. By allowing re-identification, pseudonymization enables individual-level analysis and data linkage. However, this comes with a higher risk of privacy breaches compared to anonymization.\\n\\n### Python Implementations: Strengths and Weaknesses\\n\\nWhen it comes to implementing these techniques in Python, both have their strengths and weaknesses.\\n\\nAnonymization, as demonstrated with the cryptographic hashing example, provides strong privacy guarantees and is straightforward to implement. However, its irreversible nature means that it loses the ability to link back to the original data, limiting its utility in certain scenarios.\\n\\nPseudonymization, as shown through the use of Python\u2019s `uuid` library, is also easy to implement and allows for re-identification, maintaining a higher level of data utility. However, it can be vulnerable to linkage attacks if not properly managed, and the responsibility to protect the pseudonym-to-identity mapping falls on the data handler.\\n\\n## Practical Tips and Scenarios\\n\\nAs we\u2019ve seen, both anonymization and pseudonymization have their strengths and weaknesses. Choosing the right technique depends on the specific requirements of your data project. To help you make the right decision, let\u2019s discuss some practical tips and scenarios.\\n\\n### Practical Tips for Responsible Data Handling\\n\\n1. **Understand Your Data:** Before you decide on a data protection technique, it\u2019s crucial to understand your data. What kind of data are you dealing with? What level of privacy protection does it need? Answering these questions will help you choose the right technique.\\n2. **Know Your Legal Obligations:** Depending on your jurisdiction and the nature of your data, you may be subject to specific data protection laws. Make sure you\u2019re aware of these laws and that your data handling practices comply with them.\\n3. **Secure Your Pseudonymization Keys:** If you\u2019re using pseudonymization, remember that your pseudonyms can be reversed to reveal the original data. Therefore, it\u2019s crucial to secure your pseudonymization keys. If these keys fall into the wrong hands, your data\u2019s privacy could be compromised.\\n4. **Consider the Trade-off:** Remember that there\u2019s a trade-off between privacy protection and data utility. Anonymization provides stronger privacy protection but limits data utility, while pseudonymization maintains data utility but comes with a higher risk of privacy breaches.\\n\\n### Scenarios for Anonymization and Pseudonymization\\n\\n1. **Public Data Release:** If you\u2019re releasing a dataset to the public, anonymization is your best bet. This will ensure that individuals in the dataset cannot be identified, protecting their privacy.\\n2. **Longitudinal Studies:** For longitudinal studies that require tracking individual subjects over time, pseudonymization is a good choice. It allows you to protect your subjects\u2019 privacy while maintaining the ability to link their data across time points.\\n3. **Customer Relationship Management:** In scenarios where you need to maintain a relationship with your data subjects, such as in customer relationship management, pseudonymization can be beneficial. It allows you to protect your customers\u2019 privacy while still being able to identify them when necessary.\\n\\nBy now, you should have a deep understanding of both anonymization and pseudonymization, and you should be equipped with practical knowledge to implement these techniques in your own data science endeavors. Remember, data privacy is a journey, not a destination. As data handlers, it\u2019s our responsibility to continuously optimize our data protection techniques using the powerful tools that Python provides."},{"id":"/ethics","metadata":{"permalink":"/portfolio/blog/ethics","source":"@site/blog/ethics.md","title":"Exploring AI Ethics: 15 Key Q&As","description":"Bias and Fairness in AI","date":"2024-04-05T14:49:00.000Z","formattedDate":"April 5, 2024","tags":[{"label":"AI","permalink":"/portfolio/blog/tags/ai"},{"label":"Ethics","permalink":"/portfolio/blog/tags/ethics"},{"label":"Bias","permalink":"/portfolio/blog/tags/bias"},{"label":"Transparency","permalink":"/portfolio/blog/tags/transparency"},{"label":"Privacy","permalink":"/portfolio/blog/tags/privacy"}],"readingTime":15.95,"hasTruncateMarker":true,"authors":[{"name":"Morteza Hosseini","title":"Data scientist / ML engineer","key":"mori"}],"frontMatter":{"title":"Exploring AI Ethics: 15 Key Q&As","authors":["mori"],"tags":["AI","Ethics","Bias","Transparency","Privacy"]},"unlisted":false,"prevItem":{"title":"Anonymization vs. Pseudonymization","permalink":"/portfolio/blog/anonym-pseudonym"},"nextItem":{"title":"Get-Rich-Quick Schemes are a Bad Idea","permalink":"/portfolio/blog/get-rich-quick"}},"content":"## Bias and Fairness in AI\\n\\nIn the realm of artificial intelligence, addressing the issue of bias and ensuring fairness in algorithms is paramount. AI systems have the potential to perpetuate and even amplify societal biases if not carefully designed and monitored. Exploring this topic delves into the ethical responsibility of AI researchers and practitioners to mitigate biases and promote equity in AI applications.\\n\\n### Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?\\n\\nAddressing bias and ensuring fairness in AI is of paramount importance in real-world applications because these technologies are increasingly integrated into _decision-making_ processes across various domains, from finance and healthcare to criminal justice. When AI models exhibit bias, they can perpetuate and even exacerbate _societal inequalities_. For example, biased algorithms in lending can lead to discrimination against marginalized groups, and biased facial recognition systems may lead to wrongful arrests. These issues not only _undermine trust in AI_ but also have real-life consequences for individuals and communities. Thus, addressing bias and ensuring fairness is an ethical imperative, promoting equity, and mitigating the harmful impact of AI on vulnerable populations.\\n\\n\x3c!--truncate--\x3e\\n\\nAdditionally, from a business perspective, failing to address bias and fairness concerns can result in legal and reputational risks. Companies that deploy biased AI systems may face lawsuits and damage to their brand image. Therefore, it\u2019s not only a moral obligation but also a strategic imperative for organizations to invest in addressing bias and ensuring fairness in their AI technologies. Ultimately, by actively working to eliminate bias and enhance fairness, we can harness the potential of AI for the betterment of society and avoid reinforcing existing inequalities.\\n\\n### Q2: How can data scientists proactively tackle bias in AI systems during the development process?\\n\\nData scientists can proactively tackle bias in AI systems during the development process through a multi-faceted approach. Firstly, they should conduct rigorous _data preprocessing_ to identify and mitigate bias in training data. This involves not only using representative datasets but also carefully examining historical data for any existing biases. Additionally, it\u2019s crucial to involve diverse teams in the development process to bring different perspectives and ensure comprehensive bias detection.\\n\\nFurthermore, transparency and explainability play a pivotal role. Data scientists should select interpretable algorithms and develop model-agnostic methods for explaining AI decisions. This not only aids in identifying bias but also helps build trust by allowing end-users to understand how decisions are made. Regularly auditing and re-evaluating AI systems post-deployment is also vital to ensure ongoing fairness and address emerging biases as they arise. Lastly, ethical considerations should be embedded into the design process, including defining clear fairness metrics and establishing guidelines for addressing bias. By integrating these strategies into the AI development lifecycle, data scientists and engineers can work towards creating more ethical and fair AI systems.\\n\\n### Q3: What role do regulatory frameworks and industry standards play in mitigating bias and promoting fairness in AI?\\n\\nRegulatory frameworks and industry standards play a crucial role in mitigating bias and promoting fairness in AI. Governments and organizations worldwide are recognizing the ethical challenges posed by AI, and as a result, they are developing guidelines and regulations to address these concerns. For instance, the European Union\u2019s General Data Protection Regulation ([GDPR](https://gdpr-info.eu/)) includes provisions related to automated decision-making, which impacts AI systems. Similarly, the United States is considering legislation aimed at improving AI transparency and accountability (see e.g. [CCPA](https://oag.ca.gov/privacy/ccpa)). These regulations often require organizations to be transparent about their AI systems, conduct _bias audits_, and ensure fairness in decision-making processes. By imposing legal obligations, such frameworks incentivize companies to prioritize fairness in AI development and reduce the risk of discriminatory outcomes. Furthermore, industry standards, like those developed by organizations such as IEEE or the Partnership on AI, provide best practices and guidelines for ethical AI development, serving as a valuable resource for data scientists and engineers seeking to address bias and promote fairness.\\n\\n## Transparency and Explainability\\n\\nThe increasing complexity of AI models raises fundamental questions about transparency and explainability. As AI becomes more integrated into our lives, it\u2019s essential to understand how these systems arrive at their decisions. This category examines the ethical imperative of making AI processes understandable and interpretable, fostering trust between technology and society.\\n\\n### Q4: Why is transparency and explainability essential in the development and deployment of AI systems?\\n\\nTransparency and explainability are essential in the development and deployment of AI systems for several critical reasons. Firstly, they foster trust and accountability. When individuals can understand how AI systems arrive at their decisions, it becomes easier to _trust and validate those decisions_. This is especially important in high-stakes applications like healthcare or autonomous vehicles, where transparency can be a matter of life and death. Secondly, transparency helps uncover biases and errors within AI models. By providing insight into the decision-making process, it allows data scientists and engineers to identify and correct problematic patterns in the data or algorithms. Thirdly, from a regulatory perspective, many countries are introducing laws and regulations that require explanations for automated decisions. Compliance with these regulations is crucial to avoid legal repercussions. Overall, transparency and explainability are not just ethical imperatives but also practical necessities for the responsible development and deployment of AI technology.\\n\\n### Q5: How can data practitioners ensure transparency and explainability in complex deep learning models, often considered \u201cblack boxes\u201d?\\n\\nEnsuring transparency and explainability in complex deep learning models, often regarded as \u201cblack boxes,\u201d is indeed a challenging task. However, it\u2019s not impossible, and there are several strategies that data scientists and engineers can employ. Firstly, they can use _model-agnostic techniques for interpretability_. These methods, such as [LIME](https://github.com/marcotcr/lime) (Local Interpretable Model-agnostic Explanations) or [SHAP](https://github.com/shap/shap) (SHapley Additive exPlanations), can provide insights into the model\u2019s decision-making process without needing to fully understand the complex neural network. Secondly, researchers are actively working on developing more interpretable deep learning architectures. These models are designed with transparency and explainability in mind and can provide clearer insights into the features and patterns the model is using to make decisions. Additionally, creating transparency in the data pipeline is crucial. Documenting data sources, preprocessing steps, and feature engineering can help explain why certain decisions were made based on the input data. Lastly, adopting industry standards and best practices for transparency can guide data scientists and engineers in their efforts to make AI more transparent and explainable, even in complex deep learning scenarios.\\n\\n### Q6: What challenges can arise when implementing transparency and explainability in AI systems, and how can they be overcome?\\n\\nImplementing transparency and explainability in AI systems can pose several challenges. One common challenge is the _trade-off between model complexity and interpretability_. Deep learning models, for example, can be highly accurate but difficult to interpret. To overcome this, data scientists must strike a balance between model performance and the need for transparency. Additionally, ensuring real-time explainability in applications like autonomous vehicles or medical diagnosis can be challenging, as explanations need to be provided instantly. Addressing this challenge requires the development of efficient and fast-explaining techniques. Furthermore, as AI systems evolve and adapt, maintaining transparency becomes an ongoing challenge. Data scientists need to implement continuous _monitoring_ and auditing processes to ensure that models remain transparent and explainable as they are updated. Finally, there may be resistance from organizations or stakeholders who are concerned that transparency could reveal proprietary or sensitive information. Addressing this challenge involves finding ways to provide explanations without divulging confidential data, possibly through the use of _privacy-preserving techniques_ or summarized explanations.\\n\\n## Data Privacy and Security\\n\\nProtecting data privacy and ensuring security in AI applications is an ethical obligation that AI practitioners must prioritize. As we leverage vast amounts of personal data to train AI models, the potential for misuse and breaches grows. This topic dives into the critical need to establish robust safeguards and ethical practices to safeguard individuals\u2019 privacy and protect against data breaches.\\n\\n### Q7: Why is data privacy and security a significant concern in the context of AI, and what are the potential risks of neglecting these aspects?\\n\\nData privacy and security are significant concerns in the context of AI because AI systems rely heavily on large and often sensitive datasets. Neglecting these aspects can lead to several potential risks. Firstly, there\u2019s the _risk of unauthorized access_ and data breaches, which can result in the exposure of personal information, financial records, or other sensitive data, leading to identity theft, financial fraud, or privacy violations. Secondly, when AI models are trained on data that contains personally identifiable information ([PII](https://en.wikipedia.org/wiki/Personal_data)), there\u2019s a risk of unintentional disclosure of private information during the model\u2019s operation, especially if it\u2019s used in applications like healthcare or finance. Additionally, the misuse of AI technology, if data privacy and security measures are not in place, can result in discriminatory practices, reinforcing existing biases, and harming vulnerable populations. Finally, from a regulatory perspective, non-compliance with data protection laws, such as GDPR or CCPA, can result in significant legal and financial penalties. Therefore, prioritizing data privacy and security in AI is not only an ethical imperative but also a legal and reputational necessity.\\n\\n### Q8: How can data scientists and engineers ensure robust data privacy and security in AI projects, especially when working with sensitive data?\\n\\nEnsuring robust data privacy and security in AI projects, especially when dealing with sensitive data, requires a comprehensive approach. Firstly, data scientists and engineers should implement strong _data anonymization_ and _encryption_ techniques during data collection, storage, and transmission. This prevents unauthorized access and protects the confidentiality of the data. Secondly, access control mechanisms must be in place to restrict who can access and modify the data and AI models. _Role-based access control_ and authentication methods can be effective in this regard. Thirdly, _data minimization_ should be a guiding principle. Collect only the data necessary for the specific AI task, reducing the risk associated with excessive data exposure. Additionally, regular security audits and vulnerability assessments should be conducted to identify and address potential weaknesses in the AI system. Collaboration with cybersecurity experts is crucial to stay ahead of evolving threats.\\nFurthermore, compliance with data protection regulations should be a priority. Data scientists and engineers should be well-versed in the legal requirements of the regions where their AI systems will operate and ensure that their projects adhere to these standards. Finally, ongoing monitoring and incident response plans should be established to swiftly detect and address any security breaches or privacy violations. By adopting these practices, data scientists and engineers can significantly enhance data privacy and security in AI projects, safeguarding both individuals\u2019 sensitive information and the integrity of AI applications.\\n\\n### Q9: How can organizations balance data utilization for AI advancement with respecting individual data privacy rights?\\n\\nStriking a balance between utilizing valuable data for AI advancements and respecting individual data privacy rights is a delicate but essential task. Organizations can begin by implementing _data anonymization_ techniques that allow them to use data for AI training and analytics without exposing individuals\u2019 identities. This approach ensures that the data retains its value for insights while minimizing the risks to privacy. Additionally, adopting the principle of _data minimization_ is crucial. Collect and store only the data necessary for specific AI tasks, reducing the amount of potentially sensitive information at risk. Moreover, organizations can prioritize transparency by clearly communicating their data usage and privacy policies to users. Providing individuals with informed consent and opt-in choices empowers them to make decisions about how their data is used in AI applications. Lastly, investing in state-of-the-art security measures and compliance with data protection regulations is non-negotiable. This not only safeguards data but also ensures organizations are legally compliant, thereby mitigating risks associated with data breaches and privacy violations. In essence, finding the right balance between data utilization and privacy protection involves a combination of technical, ethical, and legal strategies.\\n\\n## Accountability and Legal Frameworks\\n\\nAccountability is a central concern in the AI landscape. Determining who is responsible when AI systems fail or cause harm is a complex ethical challenge. This topic explores the development of legal frameworks and ethical standards to hold both individuals and organizations accountable for the consequences of AI technology.\\n\\n### Q10: Why is establishing accountability in AI crucial, and how does it relate to ethical considerations?\\n\\nEstablishing accountability in AI is crucial because it ensures that individuals and organizations are held _responsible_ for the consequences of AI system behavior. This accountability is directly linked to ethical considerations as it promotes transparency, fairness, and the responsible use of AI technology. When accountability is established, it becomes clear who is responsible for designing, developing, and deploying AI systems. This clarity is essential for addressing issues such as bias, discrimination, and harm caused by AI. Ethical considerations demand that those who create and operate AI systems are aware of their ethical obligations and take steps to prevent negative outcomes. Without accountability, it\u2019s challenging to enforce ethical standards and ensure that AI systems serve the best interests of society as a whole.\\n\\n### Q11: How can legal frameworks address AI challenges, and what\u2019s the role of international collaboration?\\n\\nLegal frameworks play a vital role in addressing the complex and evolving challenges posed by AI. These frameworks can provide clear guidelines, standards, and _enforcement mechanisms_ for ethical AI development and usage. To effectively address AI-related challenges, legal frameworks should be adaptable and capable of keeping pace with rapidly evolving technology. They should cover various aspects, including data protection, transparency, accountability, and liability. Additionally, international collaborations are essential in this context. AI does not respect national borders, and many AI applications are developed and deployed globally. Collaborative efforts between countries can help harmonize AI regulations, making it easier for organizations to navigate the legal landscape. Organizations like the United Nations and regional bodies such as the European Union have started working on frameworks and guidelines to address AI ethics and governance on a global scale. These collaborations facilitate the sharing of best practices and help establish a unified approach to AI ethics, ultimately fostering a more responsible and accountable AI ecosystem.\\n\\n### Q12: How can organizations and AI practitioners proactively integrate accountability into their AI development processes?\\n\\nOrganizations and AI practitioners can proactively integrate accountability into their AI development processes by adopting a holistic approach that encompasses several key steps. First and foremost, they should establish clear lines of responsibility within their teams. This includes designating individuals or teams accountable for AI ethics and compliance, ensuring that ethical considerations are integrated from the project\u2019s inception. Additionally, they should conduct comprehensive _ethical impact assessments_ throughout the AI development lifecycle, identifying potential risks and biases and taking steps to mitigate them. This may involve using fairness-aware algorithms, data audits, and bias mitigation techniques.\\n\\nFurthermore, organizations can foster a culture of ethical AI by providing training and awareness programs for their employees, promoting ethical decision-making, and incentivizing responsible AI practices. _Regularly monitoring_ AI systems post-deployment and collecting user feedback can help identify and rectify ethical concerns as they arise. Lastly, organizations should be prepared to engage with external stakeholders, including regulatory bodies, to demonstrate their commitment to accountability and compliance with legal frameworks. By integrating accountability at every stage of AI development, organizations and practitioners can ensure that ethical considerations are not an _afterthought_ but a fundamental part of their AI processes.\\n\\n## Ethical Considerations in AI R&D\\n\\nThe research and development of AI technologies come with their own set of ethical considerations. These encompass the choices made during the design and creation of AI systems, from data collection methods to algorithmic decision-making. Examining this topic sheds light on the ethical responsibilities that researchers and engineers bear throughout the AI development lifecycle.\\n\\n### Q13: Why must ethical considerations be part of AI R&D from the project\u2019s outset?\\n\\nIt\u2019s crucial for researchers and engineers to consider ethical implications during the early stages of AI R&D because ethical considerations are intertwined with the entire AI development process. Neglecting ethics in the early stages can lead to unintended consequences down the line. Ethical issues, such as bias or privacy concerns, often originate from decisions made in data collection, algorithm design, and model training. By addressing these concerns at the outset, researchers can mitigate potential harm and bias. Additionally, ethical considerations are essential for _building trust with users_ and stakeholders. Users are more likely to adopt and trust AI systems that have been developed with ethics in mind. Furthermore, considering ethics from the beginning helps prevent costly rework and legal issues that may arise if ethical concerns are only addressed after an AI system has been deployed. In essence, incorporating ethics into AI R&D from the start is not only responsible but also practical for ensuring the long-term success and impact of AI projects.\\n\\n### Q14: How can researchers handle ethics in AI development with emerging tech like deep learning?\\n\\nNavigating the ethical challenges associated with emerging technologies like deep learning and reinforcement learning requires a proactive and multidimensional approach. Firstly, researchers and engineers should prioritize _transparency_ and _explainability_, even in complex models. While deep learning models can be challenging to interpret, efforts should be made to make their decisions understandable. Model-agnostic techniques and interpretability tools can help provide insights into the inner workings of these models. Secondly, they should be aware of the potential for unintended biases to emerge in the data used to train these models. Careful data preprocessing, bias audits, and diverse datasets can help mitigate this risk. Thirdly, involving multidisciplinary teams is essential. Ethicists, social scientists, and domain experts can provide valuable perspectives on the ethical implications of AI technologies and help shape responsible development. Moreover, staying informed about emerging ethical guidelines and standards in the AI field is vital, as these guidelines evolve alongside technology. Finally, considering the long-term impact of AI systems and their potential societal consequences is essential when working with emerging technologies. Researchers and engineers should ask critical questions about the implications of their work and anticipate potential ethical dilemmas, aiming for solutions that prioritize fairness, transparency, and the well-being of society as a whole.\\n\\n### Q15: How can researchers and engineers balance the pursuit of technological innovation in AI R&D with ethical considerations?\\n\\nBalancing technological innovation with ethical considerations in AI R&D requires a mindful approach. Researchers and engineers can begin by setting clear ethical goals and principles at the project\u2019s inception. This involves _defining ethical boundaries_ and ensuring that innovation aligns with these principles. Regular ethical reviews and impact assessments can help identify potential conflicts between innovation and ethical considerations. Moreover, interdisciplinary collaboration is invaluable. By involving ethicists, social scientists, and experts from various fields, teams can benefit from diverse perspectives that help steer innovation in ethically responsible directions. Additionally, organizations can establish dedicated _ethical review boards or committees_ that evaluate projects from an ethical standpoint, providing guidance and ensuring that innovation aligns with ethical norms. Lastly, transparency is key. Researchers and engineers should communicate their ethical commitments and progress to stakeholders, including the public, to build trust and demonstrate a commitment to responsible innovation. Ultimately, ethical considerations and innovation are not mutually exclusive but should be mutually reinforcing, with ethical boundaries guiding and enhancing the impact of technological advances."},{"id":"/get-rich-quick","metadata":{"permalink":"/portfolio/blog/get-rich-quick","source":"@site/blog/get-rich-quick.md","title":"Get-Rich-Quick Schemes are a Bad Idea","description":"Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there\u2019s no shortcut to success in data science.","date":"2024-04-05T14:49:00.000Z","formattedDate":"April 5, 2024","tags":[{"label":"Data Science","permalink":"/portfolio/blog/tags/data-science"},{"label":"Machine Learning","permalink":"/portfolio/blog/tags/machine-learning"},{"label":"Get Rich Quick","permalink":"/portfolio/blog/tags/get-rich-quick"},{"label":"Mythology","permalink":"/portfolio/blog/tags/mythology"},{"label":"AI","permalink":"/portfolio/blog/tags/ai"}],"readingTime":7.24,"hasTruncateMarker":true,"authors":[{"name":"Morteza Hosseini","title":"Data scientist / ML engineer","key":"mori"}],"frontMatter":{"title":"Get-Rich-Quick Schemes are a Bad Idea","authors":["mori"],"tags":["Data Science","Machine Learning","Get Rich Quick","Mythology","AI"]},"unlisted":false,"prevItem":{"title":"Exploring AI Ethics: 15 Key Q&As","permalink":"/portfolio/blog/ethics"},"nextItem":{"title":"Advanced Guidelines for ML Model Training","permalink":"/portfolio/blog/guideline-ml"}},"content":"Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there\u2019s no shortcut to success in data science.\\n\\nLet\u2019s face it, we all want to be successful and financially stable. And with the booming field of data science and machine learning, it\u2019s tempting to believe that we can achieve our financial dreams by simply learning a few skills and jumping on the bandwagon. Unfortunately, the reality is far from that.\\n\\nHere, we\u2019ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let\u2019s debunk some myths!\\n\\n\x3c!--truncate--\x3e\\n\\n## Myth #1: You can become a data science expert in a few weeks\\n\\nOne of the most common promises of get-rich-quick schemes is that you can become a \u201cdata science expert\u201d in just a few weeks or months. They claim that their courses or bootcamps can teach you everything you need to know to land a high-paying job or start your own business.\\n\\nThe reality is that data science is a complex and multidisciplinary field that requires years of study, practice, and experimentation to master. It involves not only technical skills like programming and machine learning but also soft skills like communication, critical thinking, and problem-solving.\\n\\nMoreover, data science is constantly evolving, with new tools, techniques, and applications emerging every day. So, even if you manage to learn the basics of data science in a short period, you\u2019ll still need to keep learning and updating your skills throughout your career.\\n\\n## Myth #2: You can make a lot of money quickly with data science\\n\\nAnother myth is that you can make a lot of money quickly by working as a data scientist, consultant, or entrepreneur. They showcase success stories of people who have earned six or seven-figure salaries or built multi-million-dollar businesses in a short time.\\n\\nThe reality is that while data science can be a lucrative field, it\u2019s not a guarantee of wealth and success. Your earning potential depends on many factors, such as your skills, experience, location, industry, and demand. Moreover, there\u2019s a lot of competition in the data science job market, with many qualified candidates vying for the same positions.\\n\\nIn addition, starting a data science business is not easy, and it requires a lot of planning, investment, and risk-taking. You\u2019ll need to have a solid business plan, a clear value proposition, a strong team, and a scalable product or service. Even then, success is not guaranteed, and you\u2019ll need to work hard and smart to attract customers and grow your business.\\n\\n## Myth #3: You can skip the basics and focus only on the latest tools and trends\\n\\nA common myth perpetuated by get-rich-quick schemes is that you can skip the basics and focus only on the latest tools and trends. They claim that you don\u2019t need to learn statistics, linear algebra, or calculus, as long as you know how to use popular machine learning libraries like TensorFlow, PyTorch, or scikit-learn.\\n\\nThe reality is that while it\u2019s essential to keep up with the latest tools and trends in data science, you can\u2019t skip the fundamentals. Statistics, linear algebra, and calculus are the building blocks of data science, and without a solid understanding of them, you\u2019ll struggle to solve real-world problems and communicate your findings effectively.\\n\\nMoreover, knowing how to use a specific tool or library is not enough to be a successful data scientist. You need to be able to choose the right tool for the job, understand its strengths and limitations, and be able to customize it to fit your specific needs. That requires a deep understanding of the underlying concepts and principles of data science.\\n\\n## Myth #4: You can rely solely on online courses and tutorials\\n\\nThe next promise is that you can rely solely on online courses and tutorials to learn everything you need to know about data science. They claim that you don\u2019t need to attend a traditional university or obtain a degree or certification to become a successful data scientist.\\n\\nThe reality is that while online courses and tutorials can be a valuable source of information and inspiration, they\u2019re not a substitute for formal education or hands-on experience. Data science is a complex and dynamic field that requires a deep and broad understanding of multiple disciplines, including mathematics, statistics, computer science, and domain expertise.\\n\\nMoreover, online courses and tutorials are often limited in scope and quality, and they can\u2019t provide you with the same level of feedback, mentorship, and networking opportunities as a traditional university or professional program. They also can\u2019t expose you to the real-world challenges and complexities of data science projects, which often involve messy, incomplete, and unstructured data, conflicting goals and priorities, and ethical and legal considerations.\\n\\nTherefore, if you\u2019re serious about pursuing a career in data science, it\u2019s recommended to obtain a degree or certification from a reputable institution or professional organization. This will not only provide you with a solid foundation in data science but also enhance your credibility, employability, and earning potential.\\n\\n## Myth #5: You can automate everything with machine learning\\n\\nOne of the biggest myths perpetuated by some proponents of machine learning is that you can automate everything with it. They claim that machine learning can replace human experts and decision-makers in various domains, such as healthcare, finance, and transportation, and make better and faster decisions based on data.\\n\\nThe reality is that while machine learning can automate some tasks and improve some decisions, it cannot replace human judgment and ethical considerations. Machine learning models are only as good as the data they\u2019re trained on and the assumptions they make, and they can suffer from biases, errors, and uncertainties that can lead to incorrect or unfair decisions.\\n\\nMoreover, machine learning models can\u2019t explain how they arrived at their decisions or provide context and nuance that human experts can. Therefore, it\u2019s important to use them as a complement to human expertise and judgment, and to ensure that the models are transparent, explainable, and aligned with ethical and social values.\\n\\n## Myth #6: Big data is always better than small data\\n\\nAnother myth perpetuated by some proponents of data science is that big data is always better than small data. They claim that more data leads to more accurate and reliable models and insights, and that small data is insufficient and irrelevant for modern data science.\\n\\nThe reality is that while big data can provide more samples and variety of data, it can also introduce noise, redundancy, and complexity that can make analysis and modeling more difficult and less interpretable. Moreover, big data can pose ethical and privacy challenges, such as data ownership, consent, and security, that require careful consideration and management.\\n\\nTherefore, it\u2019s important to choose the appropriate data size and quality for the problem at hand, and to balance the trade-offs between data quantity and data quality, as well as the cost and benefit of collecting and processing more data.\\n\\n## Myth #7: Deep learning is the only type of machine learning\\n\\nDeep learning, a type of machine learning that involves neural networks with many layers, has gained a lot of attention and success in recent years, especially in domains such as image processing, speech recognition, and natural language processing. However, some people believe that deep learning is the only type of machine learning and that it\u2019s the ultimate solution to all machine learning problems.\\n\\nThe reality is that deep learning is only one type of machine learning, and it\u2019s not always the most suitable or efficient one. Other types of machine learning, such as random forests, support vector machines, and logistic regression, have their own strengths and weaknesses and can be more appropriate for different types of problems and data.\\n\\nMoreover, deep learning requires a lot of data, computation, and expertise to train and tune, and it can suffer from overfitting, generalization, and interpretability issues. So, it\u2019s important to choose the appropriate machine learning algorithm and architecture based on the problem at hand, the data characteristics, and the available resources and expertise.\\n\\n## Conclusion\\n\\nGet-rich-quick schemes in data science are based on false promises, misleading claims, and dangerous myths. They exploit people\u2019s aspirations and fears, and they undermine the integrity and credibility of data science as a profession and a discipline. As aspiring data scientists, we should reject these schemes and instead embrace the principles of honesty, curiosity, and responsibility, and strive to contribute to the advancement and the positive impact of data science on society."},{"id":"/guideline-ml","metadata":{"permalink":"/portfolio/blog/guideline-ml","source":"@site/blog/guideline-ml.md","title":"Advanced Guidelines for ML Model Training","description":"Understanding Model Training","date":"2024-04-05T14:49:00.000Z","formattedDate":"April 5, 2024","tags":[{"label":"Machine Learning","permalink":"/portfolio/blog/tags/machine-learning"},{"label":"Model Training","permalink":"/portfolio/blog/tags/model-training"},{"label":"Guidelines","permalink":"/portfolio/blog/tags/guidelines"},{"label":"Data Science","permalink":"/portfolio/blog/tags/data-science"},{"label":"Neural Networks","permalink":"/portfolio/blog/tags/neural-networks"}],"readingTime":12.375,"hasTruncateMarker":true,"authors":[{"name":"Morteza Hosseini","title":"Data scientist / ML engineer","key":"mori"}],"frontMatter":{"title":"Advanced Guidelines for ML Model Training","authors":["mori"],"tags":["Machine Learning","Model Training","Guidelines","Data Science","Neural Networks"]},"unlisted":false,"prevItem":{"title":"Get-Rich-Quick Schemes are a Bad Idea","permalink":"/portfolio/blog/get-rich-quick"},"nextItem":{"title":"Introducing Keras Core","permalink":"/portfolio/blog/keras-core"}},"content":"## Understanding Model Training\\n\\nWelcome to the captivating realm of machine learning, where algorithms breathe life into data and unveil patterns that were once hidden in the shadows. Before we dive into the intricate dance of code and data, let\u2019s take a moment to understand the essence of model training.\\n\\nImagine yourself as an artisan, crafting a masterpiece from raw materials. Just as a painter starts with a blank canvas, you begin with a dataset rich in information. This dataset is your palette, and your model is the brush that will paint the future. \ud83c\udfa8\ud83e\udd16\\n\\nModel training is the process of imbuing your creation with the ability to learn from data and make predictions. Just as a symphony conductor guides each musician to play in harmony, you guide your model through the data.\\n\\n\x3c!--truncate--\x3e\\n\\n## Preparing your data\\n\\nIn the grand symphony of model training, data preprocessing plays the role of tuning each instrument before the performance. Just as a musician wouldn\u2019t play with a broken string, your model won\u2019t perform well with unrefined data. \ud83c\udfbb\ud83c\udfbc\\n\\nLet\u2019s embark on the journey of transforming raw data into a harmonious dataset:\\n\\n- **Data Cleaning:** Begin by scrubbing your data clean. Detect and handle _missing values_, _outliers_, and inconsistencies that could disrupt your model\u2019s rhythm.\\n- **Feature Engineering:** Craft a melody of features that resonate with your model\u2019s _objective_. Engineer meaningful features that capture the essence of your problem domain.\\n- **Scaling and Normalization:** Ensure that your features are on the same scale, allowing your model to converge faster and avoid undue influence from dominant features.\\n- **Encoding:** Convert _categorical_ variables into a format that the model can understand. Whether it\u2019s _one-hot encoding_, _label encoding_, or other techniques, create a harmonious blend of categorical and numerical features.\\n- **Train-Validation-Test Split:** Divide your dataset into training, validation, and test sets. Each has a unique role in refining your model\u2019s performance.\\n- **Data Augmentation:** For image and sequence data, explore the realm of data augmentation. This technique introduces variety into your training data, enabling your model to _generalize better_.\\n\\n## Writing Efficient Training Loops for Neural Networks\\n\\nNow that your dataset is preprocessed, it\u2019s time to step into the realm of code and write the enchanting spells that will bring your model to life. Just as a playwright crafts a compelling script, you\u2019ll code a training loop that guides your model\u2019s evolution:\\n\\n- **Initialization:** Set the stage by initializing your model\u2019s architecture, loss function, and optimizer. Think of this as creating the canvas upon which your model will paint its predictions.\\n- **Epoch:** Enter the world of epochs, where each iteration weaves a new chapter of learning. Design your training loop to iterate through your dataset, presenting patterns to your model that it can decipher.\\n- **Batch:** Instead of presenting your model with the entire dataset at once, serve it bite-sized portions called batches. This _accelerates learning_ and _reduces memory consumption_.\\n- **Backpropagation:** In this step, your model learns from its mistakes by adjusting its internal parameters. Gradient descent guides it closer to accurate predictions with each step.\\n- **Validation:** Don\u2019t forget your validation set! After each epoch, serenade your model with the validation data to gauge its performance and _prevent overfitting_.\\n- **Early Stopping:** A magical trick to avoid prolonged training. If your model\u2019s performance on the validation set stagnates or worsens, elegantly halt the training and _save resources_.\\n- **Checkpoint:** Cast a checkpoint spell to save your model\u2019s progress during training. If anything goes awry, you can always _pick up where you left off_.\\n\\nAs you craft your training loop, envision yourself as a conductor guiding your model towards mastery. Each line of code is a note in the symphony of learning, harmonizing to create a melody of intelligence. Just as a conductor brings out the best from each musician, you\u2019ll coax out the finest predictions from your model, one iteration at a time. \ud83c\udfb5\ud83d\udd25\\n\\n## Optimizing Hyperparameters\\n\\nAs you embark on the journey of machine learning, you\u2019ll encounter hyperparameters&mdash;settings that wield immense influence over your model\u2019s performance. Much like a blacksmith forging a mighty sword, you\u2019ll shape these hyperparameters to enhance your model\u2019s power:\\n\\n- **Hyperspace:** Dive into the realm of hyperparameters&mdash;learning rate, batch size, hidden units, and more. Each choice alters your model\u2019s trajectory, and finding the right balance is your quest.\\n- **Grid Search:** Equip yourself with a grid search spell to systematically explore different hyperparameter combinations. Uncover the sweet spot that _maximizes_ your model\u2019s performance.\\n- **Random Search:** Embark on an adventure that balances exploration and efficiency. Randomly sample hyperparameter values to discover hidden gems without exhaustive searching.\\n- **Bayesian Optimization:** Unveil the elegance of Bayesian optimization&mdash;a smarter way to search for optimal hyperparameters. This method intelligently _narrows down the search space_ and accelerates your quest.\\n- **Regularization:** Beware the curse of overfitting on your hyperparameters. Just as knights wear armor for protection, regularize your hyperparameters to prevent them from _fitting noise_.\\n- **Automated Tuning:** Employ automated hyperparameter tuning libraries like [Optuna](https://optuna.org/) or [Hyperopt](https://hyperopt.github.io/hyperopt/). These tools streamline the search for optimal hyperparameters, freeing you to focus on your model\u2019s magic.\\n\\nIn your pursuit of optimal hyperparameters, imagine yourself as an alchemist blending rare ingredients. Each hyperparameter value is a crucial component, and the right blend will transform your model from good to extraordinary. Just as an alchemist\u2019s concoction holds secrets, your choice of hyperparameters will unlock the hidden potential within your model. \ud83e\uddea\u2728\\n\\n## Regularization Techniques\\n\\nIn the intricate tapestry of machine learning, complexity can sometimes lead to chaos. Regularization techniques are your spells to tame the unruly, ensuring that your model doesn\u2019t succumb to _overfitting_. Just as a skilled trainer guides a wild beast, you\u2019ll control the complexity of your model:\\n\\n- **Lasso and Ridge:** Explore the wonders of L1 and L2 regularization. These techniques add a touch of magic to your loss function, _penalizing overly complex models_ and ushering in simplicity.\\n- **Dropout:** Unveil the enigmatic power of dropout&mdash;a technique where neurons \u201cdisappear\u201d during training. This prevents _co-dependency_ and encourages each neuron to be self-sufficient.\\n- **Batch Normalization:** Cast a charm that stabilizes learning by normalizing the inputs of each layer. This technique keeps your model from wandering off course as it learns.\\n- **Early Stopping:** Revisit the early stopping charm from the training section. Here, it serves as a powerful regularization potion, halting training before your model becomes a slave to noise.\\n- **Data Augmentation:** Recall the data augmentation magic from data preprocessing. It\u2019s not just about variety; it also serves as a subtle form of regularization that enhances your model\u2019s ability to generalize.\\n- **Regularization:** Utilize validation data to decide which regularization techniques to employ. Just as a wise oracle provides guidance, your validation set reveals the best path to controlling complexity.\\n\\nAs you navigate the realm of regularization, think of yourself as a seasoned mage maintaining the delicate balance between power and control. Each technique you employ is a magical restraint, ensuring that your model\u2019s capabilities are channeled in the right direction. Just as a skilled magician can awe with a controlled display of magic, your model will captivate with its accuracy and generalization. \ud83c\udfa9\ud83d\udd2e\\n\\n## Choosing the Right Loss Functions\\n\\nIn the landscape of machine learning, loss functions are the compass that guides your model toward mastery. Just as a skilled navigator charts a course through uncharted waters, you\u2019ll choose the perfect loss function to steer your model towards accurate predictions:\\n\\n- **Mean Squared Error:** Begin with the classic melody of mean squared error. This loss function quantifies the distance between predictions and actual values, guiding your model toward precision.\\n- **Cross-Entropy:** This loss function elegantly captures the divergence between predicted and actual probabilities, ensuring your model learns with finesse.\\n- **Huber Loss:** A blend of mean squared error and mean absolute error, it\u2019s _robust against outliers_, helping your model navigate rough waters.\\n- **Custom Loss:** Compose your own loss function to address the unique nuances of your problem. Just as a composer tailors music to evoke specific emotions, you\u2019ll tailor your loss function to elicit accurate predictions.\\n- **Weighted Loss:** Tune the weights of your loss function to emphasize certain samples. This technique is like adjusting the volume of different instruments to achieve a balanced composition.\\n\\nAs you explore the symphony of loss functions, envision yourself as a maestro directing your model\u2019s learning journey. Each loss function is a unique melody that guides your model through the intricacies of the data landscape. Just as a maestro extracts the best from each instrument, you\u2019ll coax your model to produce predictions that resonate with accuracy. \ud83c\udfb6\ud83c\udfa4\\n\\n## Crafting Custom Neural Architectures\\n\\nAs you venture deeper into the realm of machine learning, you\u2019ll realize that models are like sculptures waiting to take shape. Default architectures might not always fit your vision perfectly. It\u2019s time to don the mantle of a sculptor and craft custom neural architectures that bring your imagination to life:\\n\\n- **The Blank Canvas:** Begin with a clear mind. Analyze your problem\u2019s nuances, data characteristics, and objectives. This blank canvas is where your creative journey starts.\\n- **Architectural Elements:** Choose the building blocks for your masterpiece. From convolutional layers for images to recurrent layers for sequences, each element has a unique role in your design.\\n- **Skip Connections:** Embrace skip connections&mdash;bridges that enable information to flow across different layers. Like secret passages in a castle, they enhance gradient flow and promote efficient learning.\\n- **Depth and Width:** Determine the depth and width of your architecture. Deeper networks capture intricate details, while wider networks handle complex relationships. Strike the right balance for your task.\\n- **Residual Networks:** Integrate residual networks (ResNets) for _smoother gradient propagation_. These magical shortcuts encourage your model to focus on learning the residual information.\\n- **Attention:** Infuse attention mechanisms to allow your model to focus on relevant parts of the input. This technique is like a spotlight that illuminates the most important features.\\n- **Reinforcement Learning:** Dive into the exciting world of neural architecture search. Just as explorers discover new lands, you\u2019ll use reinforcement learning to unearth optimal architectures.\\n\\nImagine yourself as an architect designing a grand structure. Your neural architecture is the blueprint, each layer a carefully chosen element contributing to the final form. Just as an architect molds space to evoke emotions, you\u2019ll shape your model to extract insights from data and make predictions that resonate with accuracy. \ud83c\udfdb\ufe0f\ud83d\udd25\\n\\n## The Power of Combining Models\\n\\nIn the enchanted forest of machine learning, individual models are like trees&mdash;strong, but together, they form a majestic forest. Ensembles are your magical incantations, summoning the power of multiple models to create a force that\u2019s greater than the sum of its parts:\\n\\n- **Bagging:** Bootstrap Aggregating combines predictions from diverse models, smoothing out errors and enhancing generalization.\\n- **Boosting:** Algorithms like [AdaBoost](https://en.wikipedia.org/wiki/AdaBoost) and [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) mold weak learners into a formidable force, focusing on areas where previous models stumbled.\\n- **Stacking:** Stacking combines predictions from various models, creating a meta-model that learns to weigh their expertise.\\n- **Voting Ensemble:** Gather your models for a voting ensemble. Each model casts a vote, and the most popular prediction emerges victorious. It\u2019s like the collective wisdom of a council.\\n- **Ensemble Hyperparameters:** Tune the hyperparameters of your ensemble.\\n- **Ensemble Diversity:** Embrace diversity among your models. Different architectures, algorithms, and training strategies create a symphony of perspectives that boost ensemble performance.\\n\\nEnsemble techniques are your orchestra, playing in harmony to create predictions that surpass the capabilities of any individual model. Envision yourself as a conductor orchestrating a magnificent performance, where each model contributes its unique melody to create a harmonious ensemble. Just as an ensemble elevates a performance to new heights, your ensemble of models will elevate your predictions to levels of unparalleled accuracy. \ud83c\udfbb\ud83c\udfb9\\n\\n## Leveraging Callbacks for Training Insights\\n\\nIn the labyrinth of machine learning, every training iteration is a step towards mastery. Callbacks are the lanterns that illuminate this path, providing insights and guidance throughout the training journey. Imagine yourself as an explorer equipped with these magical tools, unraveling the secrets of your model\u2019s learning process:\\n\\n- **Early Stopping:** Summon the early stopping whisperer, a callback that listens to your model\u2019s performance. It knows when to _halt training before overfitting_ casts its shadow.\\n- **Learning Rate Scheduler:** Cast a spell to adjust the learning rate as your model learns. This dynamic tuning prevents overshooting and helps converge to the optimal point.\\n- **Model Checkpoint:** Enchant your training loop with a model checkpoint charm. It saves the model\u2019s progress at intervals, ensuring you never lose the map of your journey.\\n- **Custom Callback:** Craft your own custom callbacks to fit your unique needs. Whether it\u2019s monitoring specific metrics or injecting special techniques, these callbacks are your tailored assistants.\\n\\nAs you explore the realm of callbacks, imagine yourself as a wise sage, attuned to the whispers of your model\u2019s learning journey. Each callback is a guide that imparts insights, helping you make informed decisions at every turn. Just as a sage interprets the signs of nature, you\u2019ll interpret the callbacks\u2019 cues to steer your model towards excellence. \ud83d\udd6f\ufe0f\ud83d\udd0d\\n\\n## Staying Updated with Research and Trends\\n\\nIn the ever-evolving realm of machine learning, staying stagnant is not an option. Trends shift, algorithms evolve, and new techniques emerge like shooting stars across the night sky. You are the guardian of your craft, and mastering momentum is your key to staying at the forefront:\\n\\n- **Lifelong Learning:** Take a lifelong learning oath. Just as a master craftsman hones their skills over time, commit to a journey of continuous improvement in this dynamic field.\\n- **Research Paper:** Embark on a quest to unravel research papers. Dive into the wealth of knowledge shared by the community, absorbing the latest breakthroughs and techniques.\\n- **Online Courses:** Enroll in online courses to refine your skills. These are your magical academies, offering structured lessons to help you master new technologies and methods.\\n- **Tech Conference:** Attend tech conferences and symposiums. Like a traveler exploring distant lands, immerse yourself in the sea of ideas, networking, and hands-on experience.\\n- **Blogosphere:** Contribute to the blogosphere by sharing your own insights and experiences. Just as a bard shares stories, you\u2019ll contribute to the collective knowledge of the community.\\n- **Collaboration:** Collaborate with fellow wizards&mdash;engage in discussions, exchange ideas, and collaborate on projects. The synergy of minds is your catalyst for growth.\\n- **Model Zoo Exploration:** Explore pre-trained models and libraries. These treasure troves of pre-built models and functions are like enchanted artifacts that save time and effort.\\n\\nImagine yourself as a celestial navigator, steering your ship through the cosmic sea of knowledge. Each trend, breakthrough, and technique is a star guiding your way. Just as a navigator charts new territories, you\u2019ll explore the uncharted horizons of machine learning, always seeking to harness the latest innovations and propel your craft forward. \ud83d\ude80\u2728"},{"id":"/keras-core","metadata":{"permalink":"/portfolio/blog/keras-core","source":"@site/blog/keras-core.md","title":"Introducing Keras Core","description":"Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.","date":"2024-04-05T14:49:00.000Z","formattedDate":"April 5, 2024","tags":[{"label":"Keras","permalink":"/portfolio/blog/tags/keras"},{"label":"PyTorch","permalink":"/portfolio/blog/tags/py-torch"},{"label":"Jax","permalink":"/portfolio/blog/tags/jax"},{"label":"Deep Learning","permalink":"/portfolio/blog/tags/deep-learning"},{"label":"Machine Learning","permalink":"/portfolio/blog/tags/machine-learning"}],"readingTime":4.32,"hasTruncateMarker":true,"authors":[{"name":"Morteza Hosseini","title":"Data scientist / ML engineer","key":"mori"}],"frontMatter":{"title":"Introducing Keras Core","authors":["mori"],"tags":["Keras","PyTorch","Jax","Deep Learning","Machine Learning"]},"unlisted":false,"prevItem":{"title":"Advanced Guidelines for ML Model Training","permalink":"/portfolio/blog/guideline-ml"},"nextItem":{"title":"Why is Parquet format so popular?","permalink":"/portfolio/blog/parquet"}},"content":"Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.\\n\\nExciting times lie ahead!\\n\\n## Why Use Keras Core?\\n\\nBut wait, why are they making Keras multi-backend again? Let\u2019s take a quick trip down memory lane. Not too long ago, Keras had the ability to run on multiple backends like Theano, TensorFlow, CNTK, and even MXNet. However, in 2018, they decided to focus exclusively on TensorFlow as other backends discontinued development. But times have changed! Fast forward to 2023, and we see TensorFlow dominating the production ML space with a market share of 55% to 60%. On the other hand, PyTorch has captured the ML research realm with a market share of 40% to 45%. Meanwhile, JAX, although with a smaller market share, has gained recognition from leading players in generative AI. It\u2019s clear that each framework has its strengths and user base. Keras Core enables the users to leverage the power of all three frameworks simultaneously.\\n\\nSay goodbye to framework silos and welcome the new era of multi-framework ML!\\n\\n\x3c!--truncate--\x3e\\n\\n## Exploring the Main Features of Keras Core\\n\\nNow that we understand the motivation behind Keras Core, let\u2019s dive into its incredible features.\\n\\n- **Always get the best performance for your models:** With Keras Core, you can achieve optimal training and inference performance across different hardware platforms. The benchmarks indicate that JAX usually delivers the best performance on GPU, TPU, and CPU. However, results may vary depending on the model, and non-XLA TensorFlow occasionally outperforms on GPU. The beauty of Keras Core lies in its ability to dynamically select the backend that will offer the highest efficiency for your specific model. No code changes required!\\n- **Maximize available ecosystem surface for your models:** Keras Core opens up a world of possibilities by allowing you to utilize your models with various ecosystems. Any Keras Core model can be used as a PyTorch `Module`, exported as a TensorFlow `SavedModel`, or instantiated as a stateless JAX function. This flexibility means you can leverage PyTorch ecosystem packages, TensorFlow deployment and production tools (such as TF-Serving, TF.js, and TFLite), and JAX\u2019s large-scale TPU training infrastructure. Write your `model.py` using Keras Core APIs, and unlock access to everything the ML world has to offer.\\n- **Maximize distribution for your open-source model releases:** If you want to release a pretrained model and make it accessible to as many people as possible, Keras Core is your secret weapon. By implementing your model in Keras Core, you instantly make it usable by anyone, regardless of their framework preference. While implementing in pure TensorFlow or PyTorch limits your reach to roughly half the market, Keras Core doubles your impact at no additional development cost. Get ready to leave a lasting impression!\\n- **Use data pipelines from any source:** The versatility of Keras Core shines when it comes to data pipelines. Whether you\u2019re working with `tf.data.Dataset` objects, PyTorch `DataLoader` objects, NumPy arrays, or Pandas dataframes, the Keras Core `fit()`, `evaluate()`, and `predict()` routines seamlessly integrate with all backends. Train a Keras Core + TensorFlow model on a PyTorch `DataLoader` or a Keras Core + PyTorch model on a `tf.data.Dataset`. The choice is yours, and Keras Core adapts effortlessly.\\n\\n## Empowering Developers with Progressive Disclosure and a Stateless API\\n\\nThe Keras team have brought the principle of progressive disclosure of complexity to Keras Core, just like in the existing Keras API. You can start with simple workflows using `Sequential` and `Functional` models, training them with `fit()`. As your needs grow, you can easily customize different components while reusing most of your existing code. Keras Core allows you to maintain a smooth transition without hitting a complexity cliff or requiring a switch to a different set of tools. For instance, you can customize your training loop while leveraging the power of `fit()` by simply overriding the `train_step` method. The possibilities are endless!\\n\\nBut that\u2019s not all. Keras Core introduces a new stateless API for layers, models, metrics, and optimizers, catering to functional programming enthusiasts. All stateful objects in Keras now have a stateless API, making them compatible with JAX functions that require full statelessness. Layers and models, optimizers, and metrics provide `stateless_call()`, `stateless_apply()`, `stateless_update_state()` and `stateless_result()` methods, respectively. These methods offer the same functionalities as their stateful counterparts but without any side effects. You can effortlessly use them in your JAX, PyTorch, or TensorFlow workflows, ensuring a smooth and enjoyable development experience.\\n\\n## The Bottom line\\n\\nIt\u2019s time to embrace the future of deep learning frameworks with Keras Core. Unlock the potential of multi-framework ML, achieve top performance, maximize ecosystem compatibility, and enjoy the flexibility of the stateless API. Say goodbye to limitations and hello to boundless possibilities!\\n\\nHappy coding with Keras Core and may your machine learning endeavors reach new heights!"},{"id":"/parquet","metadata":{"permalink":"/portfolio/blog/parquet","source":"@site/blog/parquet.md","title":"Why is Parquet format so popular?","description":"Introduction","date":"2024-04-05T14:49:00.000Z","formattedDate":"April 5, 2024","tags":[{"label":"Data Science","permalink":"/portfolio/blog/tags/data-science"},{"label":"Parquet","permalink":"/portfolio/blog/tags/parquet"},{"label":"Data Storage","permalink":"/portfolio/blog/tags/data-storage"},{"label":"Spark","permalink":"/portfolio/blog/tags/spark"},{"label":"Pandas Dataframe","permalink":"/portfolio/blog/tags/pandas-dataframe"}],"readingTime":3.92,"hasTruncateMarker":true,"authors":[{"name":"Morteza Hosseini","title":"Data scientist / ML engineer","key":"mori"}],"frontMatter":{"title":"Why is Parquet format so popular?","authors":["mori"],"tags":["Data Science","Parquet","Data Storage","Spark","Pandas Dataframe"]},"unlisted":false,"prevItem":{"title":"Introducing Keras Core","permalink":"/portfolio/blog/keras-core"},"nextItem":{"title":"Venn Diagrams: Art & Pitfalls","permalink":"/portfolio/blog/venn"}},"content":"## Introduction\\n\\n[Parquet](https://parquet.apache.org/) is a popular columnar storage format for big data processing. It\u2019s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we\u2019ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we\u2019ll compare it to the popular pandas DataFrame.\\n\\n\x3c!--truncate--\x3e\\n\\n## History\\n\\nThe Parquet format was created in 2013 by the Apache Software Foundation\u2019s Parquet project, a collaboration between Twitter, Cloudera, and other organizations. The goal of the project was to create a columnar storage format that was optimized for big data processing and could be used with a variety of data processing frameworks such as Hadoop, Impala, and Hive. The project was a response to the growing need for a more efficient way of storing and processing large datasets as data collection and storage was rapidly increasing. Since its release, the Parquet format has become one of the most popular storage formats for big data, widely used in the industry and adopted by many companies.\\n\\n## Why so popular?\\n\\nThe first reason why Parquet is so popular is its high compression and encoding capabilities. Parquet uses a technique called columnar storage, which organizes data in a way that allows for more efficient compression and encoding. This means that data stored in Parquet format takes up less space on disk and can be read and processed more quickly.\\n\\nAnother benefit of Parquet is its support for advanced data types and encoding schemes. Parquet supports a wide range of data types, including integers, floating-point numbers, strings, and timestamps. It also supports advanced encoding schemes like dictionary encoding, which can further reduce the size of the data on disk.\\n\\nParquet also supports advanced data querying capabilities. It provides a feature called predicate pushdown, which allows query engines to filter data on disk before reading it into memory. This reduces the amount of data that needs to be read and processed, and can greatly improve query performance.\\n\\nIn addition to its high compression and encoding capabilities, Parquet is also highly interoperable. It\u2019s supported by a wide range of big data processing tools and platforms, including Hadoop, Spark, Hive, and Pig. This means that data stored in Parquet format can be easily read and processed by a wide range of tools and platforms, making it a versatile format for big data processing tasks.\\n\\nFinally, Parquet is an open-source format, which means that it can be freely used and modified by anyone. This has led to a large and active community of developers working on the format, which has helped to improve its performance and capabilities over time.\\n\\n## Parquet vs pandas DataFrame\\n\\n| Feature             | Parquet                                | pandas DataFrames                       |\\n| ------------------- | -------------------------------------- | --------------------------------------- |\\n| Storage format      | Columnar                               | Row-based                               |\\n| Compression         | High                                   | Low                                     |\\n| Encoding            | Advanced (dictionary, RLE, etc.)       | Basic                                   |\\n| Data querying       | Advanced (predicate pushdown)          | Basic                                   |\\n| Handling large data | Efficiently handle large data          | Not optimized for large data processing |\\n| Execution           | Can be executed in distributed systems | Executed on single machine              |\\n| Open-source         | Yes                                    | Yes                                     |\\n\\nAs you can see, Parquet and [pandas](https://pandas.pydata.org/) DataFrames have some similarities, but they also have some important differences. Parquet is a columnar storage format that is optimized for big data processing and provides advanced compression and encoding capabilities. On the other hand, pandas DataFrames are row-based and are more commonly used for data analysis and manipulation.\\n\\nOne of the main benefits of using Parquet is that it can greatly improve the performance and efficiency of big data processing tasks by reducing the amount of data that needs to be read and processed. pandas DataFrames, on the other hand, are not as optimized for big data processing and may not be as efficient when working with large datasets.\\n\\n![](img/parquet-row-col.jpeg)\\nhttps://timepasstechies.com/row-oriented-column-oriented-file-formats-hadoop/\\n\\nAnother benefit of Parquet is that it supports a wide range of data types and encoding schemes, which can further reduce the size of the data on disk. pandas DataFrames, on the other hand, have a limited set of data types and do not support advanced encoding schemes.\\n\\n## Conclusion\\n\\nParquet is a popular columnar storage format for big data processing due to its high compression and encoding capabilities, support for advanced data types and encoding schemes, advanced data querying capabilities, high interoperability, and the fact that it is open-source. These benefits make it a powerful and versatile format for big data processing tasks, and it\u2019s likely to continue to be widely used in the future."},{"id":"/venn","metadata":{"permalink":"/portfolio/blog/venn","source":"@site/blog/venn.md","title":"Venn Diagrams: Art & Pitfalls","description":"In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I\u2019ve encountered their allure and their limitations. In this article, we\u2019ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We\u2019ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.","date":"2024-04-05T14:49:00.000Z","formattedDate":"April 5, 2024","tags":[{"label":"Data Science","permalink":"/portfolio/blog/tags/data-science"},{"label":"Data Visualization","permalink":"/portfolio/blog/tags/data-visualization"},{"label":"Venn Diagrams","permalink":"/portfolio/blog/tags/venn-diagrams"},{"label":"Pitfalls","permalink":"/portfolio/blog/tags/pitfalls"},{"label":"Misinterpretations","permalink":"/portfolio/blog/tags/misinterpretations"}],"readingTime":4.55,"hasTruncateMarker":true,"authors":[{"name":"Morteza Hosseini","title":"Data scientist / ML engineer","key":"mori"}],"frontMatter":{"title":"Venn Diagrams: Art & Pitfalls","authors":["mori"],"tags":["Data Science","Data Visualization","Venn Diagrams","Pitfalls","Misinterpretations"]},"unlisted":false,"prevItem":{"title":"Why is Parquet format so popular?","permalink":"/portfolio/blog/parquet"}},"content":"In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I\u2019ve encountered their allure and their limitations. In this article, we\u2019ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We\u2019ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.\\n\\n\x3c!--truncate--\x3e\\n\\nThe followings are some reasons why Venn diagrams can sometimes be considered misleading.\\n\\n## Misrepresentation of Set Sizes\\n\\nVenn diagrams, often characterized by their overlapping circles, can inadvertently create a false impression of the sizes of the sets they represent. The issue arises from the inherent limitations of Venn diagrams, where the circles\u2019 sizes are typically not proportional to the actual cardinalities of the sets. This can lead to a misperception of the data, as viewers may wrongly assume that each set is of equal size or that the proportions between sets differ from reality. Such misrepresentations can have far-reaching consequences, impacting decision-making processes and resource allocation strategies. It underscores the importance of using Venn diagrams judiciously and considering alternative visualization methods when precision in conveying set sizes is paramount in data analysis.\\n\\n![Wikimedia](img/venn-missrep-set-size.jpeg)\\n\\n## Limited to Two or Three Sets\\n\\nOne of the inherent constraints of Venn diagrams is their limited applicability to visualizing relationships between only two or three sets effectively. While these diagrams excel at illustrating the intersections and overlaps between a small number of sets, they become less practical when dealing with more complex data scenarios involving multiple overlapping sets. Attempting to represent numerous sets with overlapping circles in a Venn diagram can result in a convoluted and challenging-to-interpret visualization. This limitation highlights the need for data analysts and scientists to consider alternative visualization techniques, such as Euler diagrams, when dealing with larger and more intricate datasets with numerous intersecting subsets.\\n\\n![Wikimedia](img/venn-limited.jpeg)\\n\\n## Overlapping Interpretation\\n\\nThe interpretation of overlapping regions in Venn diagrams can often be a source of confusion and misinterpretation. While these overlapping areas are intended to represent the elements shared between sets, the extent and significance of this overlap may not be immediately clear. Viewers might struggle to discern the precise number of elements in these intersections, making it challenging to draw accurate conclusions. Additionally, it may not always be apparent which sets contribute more or less to the overlap, leaving room for ambiguity in understanding the relationships between sets. This ambiguity can be particularly problematic in complex data scenarios with multiple sets, where the nuances of overlapping interpretations become even more pronounced. To address this challenge, it\u2019s essential to provide clear labels, context, and supplementary information when using Venn diagrams to ensure that viewers can accurately decipher the overlapping regions and their implications within the data.\\n\\n![](img/venn-overlap.jpeg)\\n\\n## Non-binary Data\\n\\nVenn diagrams are most effective when dealing with binary data, where elements either belong to a set or do not. However, they can become less suitable when confronted with non-binary data, which encompasses more nuanced membership or gradations of inclusion. In situations where elements can belong to multiple sets simultaneously or have varying degrees of membership, Venn diagrams may oversimplify the complexity of these relationships. Attempting to represent such data in a binary, all-or-nothing fashion within the confines of overlapping circles can lead to a loss of valuable information and potentially mislead viewers. For these scenarios, alternative visualization methods like heatmaps or network graphs may offer a more accurate depiction of the intricate interconnections and degrees of membership present in the dataset.\\n\\n## Contextual Misuse\\n\\nContextual misuse of Venn diagrams can occur when these visual tools are applied in inappropriate situations or for purposes they were not designed for. While Venn diagrams excel at representing set relationships, they may fall short when used to convey other types of data, such as numerical values or probabilistic information. Employing Venn diagrams in the wrong context can lead to misinterpretations, confusion, or a lack of clarity in conveying the intended message. It\u2019s crucial for data scientists and analysts to exercise discretion and choose the most suitable visualization method based on the nature of the data they are dealing with. By recognizing the limitations of Venn diagrams and reserving them for scenarios where they can provide meaningful insights into set relationships, we can avoid the pitfalls of contextual misuse and ensure that data visualizations effectively communicate the desired information.\\n\\n## Lack of Quantitative Information\\n\\nA notable limitation of Venn diagrams is their inherent lack of quantitative information. These diagrams are primarily designed to show the relationships and intersections between sets visually, but they do not provide numerical or quantitative data. Consequently, when relying solely on Venn diagrams, it can be challenging to ascertain the exact size of sets, compare set sizes accurately, or determine the precise proportions of elements within overlapping regions. This absence of quantitative information can limit the depth of analysis and decision-making based on Venn diagrams alone. To address this limitation, it is advisable to complement Venn diagrams with numerical data or use other types of visualizations, such as bar charts or histograms, to provide viewers with a more comprehensive and precise understanding of the dataset.\\n\\n![](img/venn-lack-quant-info.jpeg)"}]}')}}]);
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+"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[3406],{8063:e=>{e.exports=JSON.parse('{"blogPosts":[{"id":"/anonym-pseudonym","metadata":{"permalink":"/portfolio/blog/anonym-pseudonym","source":"@site/blog/anonym-pseudonym.md","title":"Anonymization vs. Pseudonymization","description":"The Yin and Yang of Data Privacy","date":"2024-04-09T10:32:08.000Z","formattedDate":"April 9, 2024","tags":[{"label":"Data Privacy","permalink":"/portfolio/blog/tags/data-privacy"},{"label":"Anonymization","permalink":"/portfolio/blog/tags/anonymization"},{"label":"Pseudonymization","permalink":"/portfolio/blog/tags/pseudonymization"},{"label":"Data Science","permalink":"/portfolio/blog/tags/data-science"},{"label":"Python","permalink":"/portfolio/blog/tags/python"}],"readingTime":7.91,"hasTruncateMarker":true,"authors":[{"name":"Morteza Hosseini","title":"Data scientist / ML engineer","key":"mori"}],"frontMatter":{"title":"Anonymization vs. Pseudonymization","authors":["mori"],"tags":["Data Privacy","Anonymization","Pseudonymization","Data Science","Python"]},"unlisted":false,"nextItem":{"title":"Exploring AI Ethics: 15 Key Q&As","permalink":"/portfolio/blog/ethics"}},"content":"## The Yin and Yang of Data Privacy\\n\\nIn the digital era, data is the new oil. It fuels innovation, drives decision-making, and propels businesses towards success. But with great power comes great responsibility. As data scientists, we must ensure that the data we handle is not misused or exploited, causing harm to the individuals it represents. Enter the twin superheroes of data protection: Anonymization and Pseudonymization.\\n\\nAnonymization and Pseudonymization are two essential techniques in the realm of data privacy. They serve as the yin and yang, balancing the need for data utility with the imperative of privacy protection. While they may seem similar at first glance, each has its distinct characteristics and use cases.\\n\\n\x3c!--truncate--\x3e\\n\\n**Anonymization** is the process of _irreversibly_ transforming data in such a way that a data subject can no longer be identified directly or indirectly. It\u2019s like a one-way ticket: once the data is anonymized, there\u2019s no going back. It\u2019s a powerful tool for protecting privacy, but it also means that certain analyses requiring linkage back to the original data are off the table.\\n\\nOn the other hand, **Pseudonymization** is a reversible process that replaces or obscures identifiable data elements within a data record with artificial identifiers or pseudonyms. It\u2019s a bit like a masquerade ball: the data subjects are concealed behind masks, but they can be re-identified if necessary. This makes pseudonymization a flexible tool that can maintain data\u2019s utility while still offering a level of privacy protection.\\n\\nIn this blog post, we\u2019ll dive deep into these two techniques, exploring their nuances, differences, and applications. So, buckle up and get ready for an exciting journey into the world of data privacy!\\n\\n## Unraveling the Mysteries of Anonymization\\n\\nAnonymization is a potent technique that can transform your data into a privacy-compliant asset. In the realm of data privacy, Anonymization refers to the process of removing or altering personally identifiable information from your data so that the individuals whom the data describe remain anonymous.\\n\\n### The Power of Anonymization\\n\\nImagine you\u2019re a data scientist working with a dataset chock-full of sensitive information. You need to share this data with your team, but you also need to ensure you\u2019re not violating any privacy laws or ethical guidelines. Anonymization comes to your rescue here. By anonymizing your data, you can share it freely without worrying about exposing personal information.\\n\\n### Python: The Data Scientist\u2019s Swiss Army Knife\\n\\nPython, with its vast array of libraries and tools, is the perfect companion for a data scientist looking to implement Anonymization. Let\u2019s take a look at a simple example using cryptographic hashing with the hashlib library.\\n\\n```python title=\\"Python\\"\\nimport hashlib\\nfrom pprint import pprint\\n\\n# Let\'s consider a simple data list\\ndata = [\\"Alice\\", \\"Bob\\", \\"Charlie\\", \\"Alice\\", \\"Bob\\", \\"Dave\\"]\\n\\n# Anonymize the data using SHA256 hashing\\nanonymized_data = [\\n    hashlib.sha256(name.encode()).hexdigest() for name in data\\n]\\n\\npprint(anonymized_data)\\n```\\n\\n```\\n[\'3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043\',\\n\'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961\',\\n\'6e81b1255ad51bb201a2b8afa9b66653297ae0217f833b14b39b5231228bf968\',\\n\'3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043\',\\n\'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961\',\\n\'809a721743350c0c49a7b444ad3aeaf1341fdd48d1bf510e08b008edab72dc70\']\\n```\\n\\nIn this example, we\u2019ve replaced each name in our data with a unique hash generated by the SHA256 algorithm. The original names can\u2019t be retrieved from these hashes, making the data effectively anonymized.\\n\\nHowever, this is a simple example. Real-world data can be much more complex and may require more sophisticated anonymization techniques. But not to worry! Python\u2019s vast ecosystem of libraries and tools has you covered no matter how complex your data might be.\\n\\n## The Art of Pseudonymization\\n\\nAfter our deep dive into anonymization, let\u2019s now turn our attention to its counterpart, Pseudonymization. In the grand tapestry of data privacy, Pseudonymization is a technique that replaces identifiable data with artificial identifiers or pseudonyms. Unlike anonymization, pseudonymization is reversible under controlled conditions, allowing data subjects to be re-identified when necessary.\\n\\n### The Flexibility of Pseudonymization\\n\\nImagine you\u2019re a healthcare researcher working on a longitudinal study. You need to track individual patients\u2019 health outcomes over time, but you also need to respect their privacy. Pseudonymization is your ally in this scenario. By replacing identifiable information like names or social security numbers with pseudonyms, you can protect your patients\u2019 privacy while still being able to track their data over time.\\n\\n### Python to the Rescue: Implementing Pseudonymization\\n\\nPython, with its diverse ecosystem of libraries and tools, makes implementing pseudonymization a breeze. Let\u2019s look at an example using Python\u2019s built-in `uuid` library to generate unique pseudonyms for our data.\\n\\n```python title=\\"Python\\"\\nimport uuid\\nfrom pprint import pprint\\n\\n# Let\'s consider a simple data list\\ndata = [\'Alice\', \'Bob\', \'Charlie\', \'Alice\', \'Bob\', \'Dave\']\\n\\n# Create a dictionary to store our pseudonyms\\npseudonyms = {}\\n\\n# Pseudonymize the data\\npseudonymized_data = [\\n    pseudonyms.setdefault(name, str(uuid.uuid4())) for name in data\\n]\\n\\npprint(pseudonymized_data)\\n```\\n\\n```\\n[\'dfaa2289-1611-44ea-950a-8c9b43544a68\',\\n\'f20c6a2f-bdc2-40b4-896a-614bec5baa77\',\\n\'9244f9fb-f21b-4111-b982-ce81f9374daf\',\\n\'dfaa2289-1611-44ea-950a-8c9b43544a68\',\\n\'f20c6a2f-bdc2-40b4-896a-614bec5baa77\',\\n\'a2697683-8e88-4b5b-add1-51cc4533ffda\']\\n```\\n\\nIn this example, we\u2019ve replaced each name in our data with a unique pseudonym generated by the `uuid` library. The pseudonyms are stored in a dictionary, allowing us to consistently replace each name with the same pseudonym across our data. However, with the dictionary, we can also reverse the pseudonymization process if needed, which is not possible with anonymization.\\n\\n```python title=\\"Python\\"\\npprint(pseudonyms)\\n```\\n\\n```\\n{\'Alice\': \'dfaa2289-1611-44ea-950a-8c9b43544a68\',\\n\'Bob\': \'f20c6a2f-bdc2-40b4-896a-614bec5baa77\',\\n\'Charlie\': \'9244f9fb-f21b-4111-b982-ce81f9374daf\',\\n\'Dave\': \'a2697683-8e88-4b5b-add1-51cc4533ffda\'}\\n```\\n\\n## Comparing Anonymization and Pseudonymization\\n\\nNow that we\u2019ve explored both anonymization and pseudonymization, let\u2019s take a step back and compare these two techniques. While they both aim to protect privacy, their differences in reversibility, data utility, and risk levels make them suitable for different scenarios.\\n\\n### Reversibility: A One-Way Street vs. A Two-Way Lane\\n\\nAnonymization is a one-way street. Once data is anonymized, it\u2019s impossible to revert it back to its original form. This makes anonymization a strong tool for privacy protection, as it eliminates the risk of re-identification.\\n\\nOn the contrary, pseudonymization is a two-way lane. It replaces identifiable data with pseudonyms, but this process can be reversed under controlled conditions. This allows for the possibility of re-identification, which can be beneficial in certain scenarios, such as longitudinal studies or customer relationship management.\\n\\n### Data Utility: A Trade-Off\\n\\nAnonymization provides a high level of privacy protection, but it comes at the cost of data utility. By irreversibly transforming data, anonymization can limit the types of analysis that can be performed. For example, it\u2019s impossible to conduct individual-level analysis or link data across multiple datasets once it\u2019s been anonymized.\\n\\nPseudonymization, on the other hand, maintains a higher level of data utility. By allowing re-identification, pseudonymization enables individual-level analysis and data linkage. However, this comes with a higher risk of privacy breaches compared to anonymization.\\n\\n### Python Implementations: Strengths and Weaknesses\\n\\nWhen it comes to implementing these techniques in Python, both have their strengths and weaknesses.\\n\\nAnonymization, as demonstrated with the cryptographic hashing example, provides strong privacy guarantees and is straightforward to implement. However, its irreversible nature means that it loses the ability to link back to the original data, limiting its utility in certain scenarios.\\n\\nPseudonymization, as shown through the use of Python\u2019s `uuid` library, is also easy to implement and allows for re-identification, maintaining a higher level of data utility. However, it can be vulnerable to linkage attacks if not properly managed, and the responsibility to protect the pseudonym-to-identity mapping falls on the data handler.\\n\\n## Practical Tips and Scenarios\\n\\nAs we\u2019ve seen, both anonymization and pseudonymization have their strengths and weaknesses. Choosing the right technique depends on the specific requirements of your data project. To help you make the right decision, let\u2019s discuss some practical tips and scenarios.\\n\\n### Practical Tips for Responsible Data Handling\\n\\n1. **Understand Your Data:** Before you decide on a data protection technique, it\u2019s crucial to understand your data. What kind of data are you dealing with? What level of privacy protection does it need? Answering these questions will help you choose the right technique.\\n2. **Know Your Legal Obligations:** Depending on your jurisdiction and the nature of your data, you may be subject to specific data protection laws. Make sure you\u2019re aware of these laws and that your data handling practices comply with them.\\n3. **Secure Your Pseudonymization Keys:** If you\u2019re using pseudonymization, remember that your pseudonyms can be reversed to reveal the original data. Therefore, it\u2019s crucial to secure your pseudonymization keys. If these keys fall into the wrong hands, your data\u2019s privacy could be compromised.\\n4. **Consider the Trade-off:** Remember that there\u2019s a trade-off between privacy protection and data utility. Anonymization provides stronger privacy protection but limits data utility, while pseudonymization maintains data utility but comes with a higher risk of privacy breaches.\\n\\n### Scenarios for Anonymization and Pseudonymization\\n\\n1. **Public Data Release:** If you\u2019re releasing a dataset to the public, anonymization is your best bet. This will ensure that individuals in the dataset cannot be identified, protecting their privacy.\\n2. **Longitudinal Studies:** For longitudinal studies that require tracking individual subjects over time, pseudonymization is a good choice. It allows you to protect your subjects\u2019 privacy while maintaining the ability to link their data across time points.\\n3. **Customer Relationship Management:** In scenarios where you need to maintain a relationship with your data subjects, such as in customer relationship management, pseudonymization can be beneficial. It allows you to protect your customers\u2019 privacy while still being able to identify them when necessary.\\n\\nBy now, you should have a deep understanding of both anonymization and pseudonymization, and you should be equipped with practical knowledge to implement these techniques in your own data science endeavors. Remember, data privacy is a journey, not a destination. As data handlers, it\u2019s our responsibility to continuously optimize our data protection techniques using the powerful tools that Python provides."},{"id":"/ethics","metadata":{"permalink":"/portfolio/blog/ethics","source":"@site/blog/ethics.md","title":"Exploring AI Ethics: 15 Key Q&As","description":"Bias and Fairness in AI","date":"2024-04-09T10:32:08.000Z","formattedDate":"April 9, 2024","tags":[{"label":"AI","permalink":"/portfolio/blog/tags/ai"},{"label":"Ethics","permalink":"/portfolio/blog/tags/ethics"},{"label":"Bias","permalink":"/portfolio/blog/tags/bias"},{"label":"Transparency","permalink":"/portfolio/blog/tags/transparency"},{"label":"Privacy","permalink":"/portfolio/blog/tags/privacy"}],"readingTime":15.95,"hasTruncateMarker":true,"authors":[{"name":"Morteza Hosseini","title":"Data scientist / ML engineer","key":"mori"}],"frontMatter":{"title":"Exploring AI Ethics: 15 Key Q&As","authors":["mori"],"tags":["AI","Ethics","Bias","Transparency","Privacy"]},"unlisted":false,"prevItem":{"title":"Anonymization vs. Pseudonymization","permalink":"/portfolio/blog/anonym-pseudonym"},"nextItem":{"title":"Get-Rich-Quick Schemes are a Bad Idea","permalink":"/portfolio/blog/get-rich-quick"}},"content":"## Bias and Fairness in AI\\n\\nIn the realm of artificial intelligence, addressing the issue of bias and ensuring fairness in algorithms is paramount. AI systems have the potential to perpetuate and even amplify societal biases if not carefully designed and monitored. Exploring this topic delves into the ethical responsibility of AI researchers and practitioners to mitigate biases and promote equity in AI applications.\\n\\n### Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?\\n\\nAddressing bias and ensuring fairness in AI is of paramount importance in real-world applications because these technologies are increasingly integrated into _decision-making_ processes across various domains, from finance and healthcare to criminal justice. When AI models exhibit bias, they can perpetuate and even exacerbate _societal inequalities_. For example, biased algorithms in lending can lead to discrimination against marginalized groups, and biased facial recognition systems may lead to wrongful arrests. These issues not only _undermine trust in AI_ but also have real-life consequences for individuals and communities. Thus, addressing bias and ensuring fairness is an ethical imperative, promoting equity, and mitigating the harmful impact of AI on vulnerable populations.\\n\\n\x3c!--truncate--\x3e\\n\\nAdditionally, from a business perspective, failing to address bias and fairness concerns can result in legal and reputational risks. Companies that deploy biased AI systems may face lawsuits and damage to their brand image. Therefore, it\u2019s not only a moral obligation but also a strategic imperative for organizations to invest in addressing bias and ensuring fairness in their AI technologies. Ultimately, by actively working to eliminate bias and enhance fairness, we can harness the potential of AI for the betterment of society and avoid reinforcing existing inequalities.\\n\\n### Q2: How can data scientists proactively tackle bias in AI systems during the development process?\\n\\nData scientists can proactively tackle bias in AI systems during the development process through a multi-faceted approach. Firstly, they should conduct rigorous _data preprocessing_ to identify and mitigate bias in training data. This involves not only using representative datasets but also carefully examining historical data for any existing biases. Additionally, it\u2019s crucial to involve diverse teams in the development process to bring different perspectives and ensure comprehensive bias detection.\\n\\nFurthermore, transparency and explainability play a pivotal role. Data scientists should select interpretable algorithms and develop model-agnostic methods for explaining AI decisions. This not only aids in identifying bias but also helps build trust by allowing end-users to understand how decisions are made. Regularly auditing and re-evaluating AI systems post-deployment is also vital to ensure ongoing fairness and address emerging biases as they arise. Lastly, ethical considerations should be embedded into the design process, including defining clear fairness metrics and establishing guidelines for addressing bias. By integrating these strategies into the AI development lifecycle, data scientists and engineers can work towards creating more ethical and fair AI systems.\\n\\n### Q3: What role do regulatory frameworks and industry standards play in mitigating bias and promoting fairness in AI?\\n\\nRegulatory frameworks and industry standards play a crucial role in mitigating bias and promoting fairness in AI. Governments and organizations worldwide are recognizing the ethical challenges posed by AI, and as a result, they are developing guidelines and regulations to address these concerns. For instance, the European Union\u2019s General Data Protection Regulation ([GDPR](https://gdpr-info.eu/)) includes provisions related to automated decision-making, which impacts AI systems. Similarly, the United States is considering legislation aimed at improving AI transparency and accountability (see e.g. [CCPA](https://oag.ca.gov/privacy/ccpa)). These regulations often require organizations to be transparent about their AI systems, conduct _bias audits_, and ensure fairness in decision-making processes. By imposing legal obligations, such frameworks incentivize companies to prioritize fairness in AI development and reduce the risk of discriminatory outcomes. Furthermore, industry standards, like those developed by organizations such as IEEE or the Partnership on AI, provide best practices and guidelines for ethical AI development, serving as a valuable resource for data scientists and engineers seeking to address bias and promote fairness.\\n\\n## Transparency and Explainability\\n\\nThe increasing complexity of AI models raises fundamental questions about transparency and explainability. As AI becomes more integrated into our lives, it\u2019s essential to understand how these systems arrive at their decisions. This category examines the ethical imperative of making AI processes understandable and interpretable, fostering trust between technology and society.\\n\\n### Q4: Why is transparency and explainability essential in the development and deployment of AI systems?\\n\\nTransparency and explainability are essential in the development and deployment of AI systems for several critical reasons. Firstly, they foster trust and accountability. When individuals can understand how AI systems arrive at their decisions, it becomes easier to _trust and validate those decisions_. This is especially important in high-stakes applications like healthcare or autonomous vehicles, where transparency can be a matter of life and death. Secondly, transparency helps uncover biases and errors within AI models. By providing insight into the decision-making process, it allows data scientists and engineers to identify and correct problematic patterns in the data or algorithms. Thirdly, from a regulatory perspective, many countries are introducing laws and regulations that require explanations for automated decisions. Compliance with these regulations is crucial to avoid legal repercussions. Overall, transparency and explainability are not just ethical imperatives but also practical necessities for the responsible development and deployment of AI technology.\\n\\n### Q5: How can data practitioners ensure transparency and explainability in complex deep learning models, often considered \u201cblack boxes\u201d?\\n\\nEnsuring transparency and explainability in complex deep learning models, often regarded as \u201cblack boxes,\u201d is indeed a challenging task. However, it\u2019s not impossible, and there are several strategies that data scientists and engineers can employ. Firstly, they can use _model-agnostic techniques for interpretability_. These methods, such as [LIME](https://github.com/marcotcr/lime) (Local Interpretable Model-agnostic Explanations) or [SHAP](https://github.com/shap/shap) (SHapley Additive exPlanations), can provide insights into the model\u2019s decision-making process without needing to fully understand the complex neural network. Secondly, researchers are actively working on developing more interpretable deep learning architectures. These models are designed with transparency and explainability in mind and can provide clearer insights into the features and patterns the model is using to make decisions. Additionally, creating transparency in the data pipeline is crucial. Documenting data sources, preprocessing steps, and feature engineering can help explain why certain decisions were made based on the input data. Lastly, adopting industry standards and best practices for transparency can guide data scientists and engineers in their efforts to make AI more transparent and explainable, even in complex deep learning scenarios.\\n\\n### Q6: What challenges can arise when implementing transparency and explainability in AI systems, and how can they be overcome?\\n\\nImplementing transparency and explainability in AI systems can pose several challenges. One common challenge is the _trade-off between model complexity and interpretability_. Deep learning models, for example, can be highly accurate but difficult to interpret. To overcome this, data scientists must strike a balance between model performance and the need for transparency. Additionally, ensuring real-time explainability in applications like autonomous vehicles or medical diagnosis can be challenging, as explanations need to be provided instantly. Addressing this challenge requires the development of efficient and fast-explaining techniques. Furthermore, as AI systems evolve and adapt, maintaining transparency becomes an ongoing challenge. Data scientists need to implement continuous _monitoring_ and auditing processes to ensure that models remain transparent and explainable as they are updated. Finally, there may be resistance from organizations or stakeholders who are concerned that transparency could reveal proprietary or sensitive information. Addressing this challenge involves finding ways to provide explanations without divulging confidential data, possibly through the use of _privacy-preserving techniques_ or summarized explanations.\\n\\n## Data Privacy and Security\\n\\nProtecting data privacy and ensuring security in AI applications is an ethical obligation that AI practitioners must prioritize. As we leverage vast amounts of personal data to train AI models, the potential for misuse and breaches grows. This topic dives into the critical need to establish robust safeguards and ethical practices to safeguard individuals\u2019 privacy and protect against data breaches.\\n\\n### Q7: Why is data privacy and security a significant concern in the context of AI, and what are the potential risks of neglecting these aspects?\\n\\nData privacy and security are significant concerns in the context of AI because AI systems rely heavily on large and often sensitive datasets. Neglecting these aspects can lead to several potential risks. Firstly, there\u2019s the _risk of unauthorized access_ and data breaches, which can result in the exposure of personal information, financial records, or other sensitive data, leading to identity theft, financial fraud, or privacy violations. Secondly, when AI models are trained on data that contains personally identifiable information ([PII](https://en.wikipedia.org/wiki/Personal_data)), there\u2019s a risk of unintentional disclosure of private information during the model\u2019s operation, especially if it\u2019s used in applications like healthcare or finance. Additionally, the misuse of AI technology, if data privacy and security measures are not in place, can result in discriminatory practices, reinforcing existing biases, and harming vulnerable populations. Finally, from a regulatory perspective, non-compliance with data protection laws, such as GDPR or CCPA, can result in significant legal and financial penalties. Therefore, prioritizing data privacy and security in AI is not only an ethical imperative but also a legal and reputational necessity.\\n\\n### Q8: How can data scientists and engineers ensure robust data privacy and security in AI projects, especially when working with sensitive data?\\n\\nEnsuring robust data privacy and security in AI projects, especially when dealing with sensitive data, requires a comprehensive approach. Firstly, data scientists and engineers should implement strong _data anonymization_ and _encryption_ techniques during data collection, storage, and transmission. This prevents unauthorized access and protects the confidentiality of the data. Secondly, access control mechanisms must be in place to restrict who can access and modify the data and AI models. _Role-based access control_ and authentication methods can be effective in this regard. Thirdly, _data minimization_ should be a guiding principle. Collect only the data necessary for the specific AI task, reducing the risk associated with excessive data exposure. Additionally, regular security audits and vulnerability assessments should be conducted to identify and address potential weaknesses in the AI system. Collaboration with cybersecurity experts is crucial to stay ahead of evolving threats.\\nFurthermore, compliance with data protection regulations should be a priority. Data scientists and engineers should be well-versed in the legal requirements of the regions where their AI systems will operate and ensure that their projects adhere to these standards. Finally, ongoing monitoring and incident response plans should be established to swiftly detect and address any security breaches or privacy violations. By adopting these practices, data scientists and engineers can significantly enhance data privacy and security in AI projects, safeguarding both individuals\u2019 sensitive information and the integrity of AI applications.\\n\\n### Q9: How can organizations balance data utilization for AI advancement with respecting individual data privacy rights?\\n\\nStriking a balance between utilizing valuable data for AI advancements and respecting individual data privacy rights is a delicate but essential task. Organizations can begin by implementing _data anonymization_ techniques that allow them to use data for AI training and analytics without exposing individuals\u2019 identities. This approach ensures that the data retains its value for insights while minimizing the risks to privacy. Additionally, adopting the principle of _data minimization_ is crucial. Collect and store only the data necessary for specific AI tasks, reducing the amount of potentially sensitive information at risk. Moreover, organizations can prioritize transparency by clearly communicating their data usage and privacy policies to users. Providing individuals with informed consent and opt-in choices empowers them to make decisions about how their data is used in AI applications. Lastly, investing in state-of-the-art security measures and compliance with data protection regulations is non-negotiable. This not only safeguards data but also ensures organizations are legally compliant, thereby mitigating risks associated with data breaches and privacy violations. In essence, finding the right balance between data utilization and privacy protection involves a combination of technical, ethical, and legal strategies.\\n\\n## Accountability and Legal Frameworks\\n\\nAccountability is a central concern in the AI landscape. Determining who is responsible when AI systems fail or cause harm is a complex ethical challenge. This topic explores the development of legal frameworks and ethical standards to hold both individuals and organizations accountable for the consequences of AI technology.\\n\\n### Q10: Why is establishing accountability in AI crucial, and how does it relate to ethical considerations?\\n\\nEstablishing accountability in AI is crucial because it ensures that individuals and organizations are held _responsible_ for the consequences of AI system behavior. This accountability is directly linked to ethical considerations as it promotes transparency, fairness, and the responsible use of AI technology. When accountability is established, it becomes clear who is responsible for designing, developing, and deploying AI systems. This clarity is essential for addressing issues such as bias, discrimination, and harm caused by AI. Ethical considerations demand that those who create and operate AI systems are aware of their ethical obligations and take steps to prevent negative outcomes. Without accountability, it\u2019s challenging to enforce ethical standards and ensure that AI systems serve the best interests of society as a whole.\\n\\n### Q11: How can legal frameworks address AI challenges, and what\u2019s the role of international collaboration?\\n\\nLegal frameworks play a vital role in addressing the complex and evolving challenges posed by AI. These frameworks can provide clear guidelines, standards, and _enforcement mechanisms_ for ethical AI development and usage. To effectively address AI-related challenges, legal frameworks should be adaptable and capable of keeping pace with rapidly evolving technology. They should cover various aspects, including data protection, transparency, accountability, and liability. Additionally, international collaborations are essential in this context. AI does not respect national borders, and many AI applications are developed and deployed globally. Collaborative efforts between countries can help harmonize AI regulations, making it easier for organizations to navigate the legal landscape. Organizations like the United Nations and regional bodies such as the European Union have started working on frameworks and guidelines to address AI ethics and governance on a global scale. These collaborations facilitate the sharing of best practices and help establish a unified approach to AI ethics, ultimately fostering a more responsible and accountable AI ecosystem.\\n\\n### Q12: How can organizations and AI practitioners proactively integrate accountability into their AI development processes?\\n\\nOrganizations and AI practitioners can proactively integrate accountability into their AI development processes by adopting a holistic approach that encompasses several key steps. First and foremost, they should establish clear lines of responsibility within their teams. This includes designating individuals or teams accountable for AI ethics and compliance, ensuring that ethical considerations are integrated from the project\u2019s inception. Additionally, they should conduct comprehensive _ethical impact assessments_ throughout the AI development lifecycle, identifying potential risks and biases and taking steps to mitigate them. This may involve using fairness-aware algorithms, data audits, and bias mitigation techniques.\\n\\nFurthermore, organizations can foster a culture of ethical AI by providing training and awareness programs for their employees, promoting ethical decision-making, and incentivizing responsible AI practices. _Regularly monitoring_ AI systems post-deployment and collecting user feedback can help identify and rectify ethical concerns as they arise. Lastly, organizations should be prepared to engage with external stakeholders, including regulatory bodies, to demonstrate their commitment to accountability and compliance with legal frameworks. By integrating accountability at every stage of AI development, organizations and practitioners can ensure that ethical considerations are not an _afterthought_ but a fundamental part of their AI processes.\\n\\n## Ethical Considerations in AI R&D\\n\\nThe research and development of AI technologies come with their own set of ethical considerations. These encompass the choices made during the design and creation of AI systems, from data collection methods to algorithmic decision-making. Examining this topic sheds light on the ethical responsibilities that researchers and engineers bear throughout the AI development lifecycle.\\n\\n### Q13: Why must ethical considerations be part of AI R&D from the project\u2019s outset?\\n\\nIt\u2019s crucial for researchers and engineers to consider ethical implications during the early stages of AI R&D because ethical considerations are intertwined with the entire AI development process. Neglecting ethics in the early stages can lead to unintended consequences down the line. Ethical issues, such as bias or privacy concerns, often originate from decisions made in data collection, algorithm design, and model training. By addressing these concerns at the outset, researchers can mitigate potential harm and bias. Additionally, ethical considerations are essential for _building trust with users_ and stakeholders. Users are more likely to adopt and trust AI systems that have been developed with ethics in mind. Furthermore, considering ethics from the beginning helps prevent costly rework and legal issues that may arise if ethical concerns are only addressed after an AI system has been deployed. In essence, incorporating ethics into AI R&D from the start is not only responsible but also practical for ensuring the long-term success and impact of AI projects.\\n\\n### Q14: How can researchers handle ethics in AI development with emerging tech like deep learning?\\n\\nNavigating the ethical challenges associated with emerging technologies like deep learning and reinforcement learning requires a proactive and multidimensional approach. Firstly, researchers and engineers should prioritize _transparency_ and _explainability_, even in complex models. While deep learning models can be challenging to interpret, efforts should be made to make their decisions understandable. Model-agnostic techniques and interpretability tools can help provide insights into the inner workings of these models. Secondly, they should be aware of the potential for unintended biases to emerge in the data used to train these models. Careful data preprocessing, bias audits, and diverse datasets can help mitigate this risk. Thirdly, involving multidisciplinary teams is essential. Ethicists, social scientists, and domain experts can provide valuable perspectives on the ethical implications of AI technologies and help shape responsible development. Moreover, staying informed about emerging ethical guidelines and standards in the AI field is vital, as these guidelines evolve alongside technology. Finally, considering the long-term impact of AI systems and their potential societal consequences is essential when working with emerging technologies. Researchers and engineers should ask critical questions about the implications of their work and anticipate potential ethical dilemmas, aiming for solutions that prioritize fairness, transparency, and the well-being of society as a whole.\\n\\n### Q15: How can researchers and engineers balance the pursuit of technological innovation in AI R&D with ethical considerations?\\n\\nBalancing technological innovation with ethical considerations in AI R&D requires a mindful approach. Researchers and engineers can begin by setting clear ethical goals and principles at the project\u2019s inception. This involves _defining ethical boundaries_ and ensuring that innovation aligns with these principles. Regular ethical reviews and impact assessments can help identify potential conflicts between innovation and ethical considerations. Moreover, interdisciplinary collaboration is invaluable. By involving ethicists, social scientists, and experts from various fields, teams can benefit from diverse perspectives that help steer innovation in ethically responsible directions. Additionally, organizations can establish dedicated _ethical review boards or committees_ that evaluate projects from an ethical standpoint, providing guidance and ensuring that innovation aligns with ethical norms. Lastly, transparency is key. Researchers and engineers should communicate their ethical commitments and progress to stakeholders, including the public, to build trust and demonstrate a commitment to responsible innovation. Ultimately, ethical considerations and innovation are not mutually exclusive but should be mutually reinforcing, with ethical boundaries guiding and enhancing the impact of technological advances."},{"id":"/get-rich-quick","metadata":{"permalink":"/portfolio/blog/get-rich-quick","source":"@site/blog/get-rich-quick.md","title":"Get-Rich-Quick Schemes are a Bad Idea","description":"Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there\u2019s no shortcut to success in data science.","date":"2024-04-09T10:32:08.000Z","formattedDate":"April 9, 2024","tags":[{"label":"Data Science","permalink":"/portfolio/blog/tags/data-science"},{"label":"Machine Learning","permalink":"/portfolio/blog/tags/machine-learning"},{"label":"Get Rich Quick","permalink":"/portfolio/blog/tags/get-rich-quick"},{"label":"Mythology","permalink":"/portfolio/blog/tags/mythology"},{"label":"AI","permalink":"/portfolio/blog/tags/ai"}],"readingTime":7.24,"hasTruncateMarker":true,"authors":[{"name":"Morteza Hosseini","title":"Data scientist / ML engineer","key":"mori"}],"frontMatter":{"title":"Get-Rich-Quick Schemes are a Bad Idea","authors":["mori"],"tags":["Data Science","Machine Learning","Get Rich Quick","Mythology","AI"]},"unlisted":false,"prevItem":{"title":"Exploring AI Ethics: 15 Key Q&As","permalink":"/portfolio/blog/ethics"},"nextItem":{"title":"Advanced Guidelines for ML Model Training","permalink":"/portfolio/blog/guideline-ml"}},"content":"Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there\u2019s no shortcut to success in data science.\\n\\nLet\u2019s face it, we all want to be successful and financially stable. And with the booming field of data science and machine learning, it\u2019s tempting to believe that we can achieve our financial dreams by simply learning a few skills and jumping on the bandwagon. Unfortunately, the reality is far from that.\\n\\nHere, we\u2019ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let\u2019s debunk some myths!\\n\\n\x3c!--truncate--\x3e\\n\\n## Myth #1: You can become a data science expert in a few weeks\\n\\nOne of the most common promises of get-rich-quick schemes is that you can become a \u201cdata science expert\u201d in just a few weeks or months. They claim that their courses or bootcamps can teach you everything you need to know to land a high-paying job or start your own business.\\n\\nThe reality is that data science is a complex and multidisciplinary field that requires years of study, practice, and experimentation to master. It involves not only technical skills like programming and machine learning but also soft skills like communication, critical thinking, and problem-solving.\\n\\nMoreover, data science is constantly evolving, with new tools, techniques, and applications emerging every day. So, even if you manage to learn the basics of data science in a short period, you\u2019ll still need to keep learning and updating your skills throughout your career.\\n\\n## Myth #2: You can make a lot of money quickly with data science\\n\\nAnother myth is that you can make a lot of money quickly by working as a data scientist, consultant, or entrepreneur. They showcase success stories of people who have earned six or seven-figure salaries or built multi-million-dollar businesses in a short time.\\n\\nThe reality is that while data science can be a lucrative field, it\u2019s not a guarantee of wealth and success. Your earning potential depends on many factors, such as your skills, experience, location, industry, and demand. Moreover, there\u2019s a lot of competition in the data science job market, with many qualified candidates vying for the same positions.\\n\\nIn addition, starting a data science business is not easy, and it requires a lot of planning, investment, and risk-taking. You\u2019ll need to have a solid business plan, a clear value proposition, a strong team, and a scalable product or service. Even then, success is not guaranteed, and you\u2019ll need to work hard and smart to attract customers and grow your business.\\n\\n## Myth #3: You can skip the basics and focus only on the latest tools and trends\\n\\nA common myth perpetuated by get-rich-quick schemes is that you can skip the basics and focus only on the latest tools and trends. They claim that you don\u2019t need to learn statistics, linear algebra, or calculus, as long as you know how to use popular machine learning libraries like TensorFlow, PyTorch, or scikit-learn.\\n\\nThe reality is that while it\u2019s essential to keep up with the latest tools and trends in data science, you can\u2019t skip the fundamentals. Statistics, linear algebra, and calculus are the building blocks of data science, and without a solid understanding of them, you\u2019ll struggle to solve real-world problems and communicate your findings effectively.\\n\\nMoreover, knowing how to use a specific tool or library is not enough to be a successful data scientist. You need to be able to choose the right tool for the job, understand its strengths and limitations, and be able to customize it to fit your specific needs. That requires a deep understanding of the underlying concepts and principles of data science.\\n\\n## Myth #4: You can rely solely on online courses and tutorials\\n\\nThe next promise is that you can rely solely on online courses and tutorials to learn everything you need to know about data science. They claim that you don\u2019t need to attend a traditional university or obtain a degree or certification to become a successful data scientist.\\n\\nThe reality is that while online courses and tutorials can be a valuable source of information and inspiration, they\u2019re not a substitute for formal education or hands-on experience. Data science is a complex and dynamic field that requires a deep and broad understanding of multiple disciplines, including mathematics, statistics, computer science, and domain expertise.\\n\\nMoreover, online courses and tutorials are often limited in scope and quality, and they can\u2019t provide you with the same level of feedback, mentorship, and networking opportunities as a traditional university or professional program. They also can\u2019t expose you to the real-world challenges and complexities of data science projects, which often involve messy, incomplete, and unstructured data, conflicting goals and priorities, and ethical and legal considerations.\\n\\nTherefore, if you\u2019re serious about pursuing a career in data science, it\u2019s recommended to obtain a degree or certification from a reputable institution or professional organization. This will not only provide you with a solid foundation in data science but also enhance your credibility, employability, and earning potential.\\n\\n## Myth #5: You can automate everything with machine learning\\n\\nOne of the biggest myths perpetuated by some proponents of machine learning is that you can automate everything with it. They claim that machine learning can replace human experts and decision-makers in various domains, such as healthcare, finance, and transportation, and make better and faster decisions based on data.\\n\\nThe reality is that while machine learning can automate some tasks and improve some decisions, it cannot replace human judgment and ethical considerations. Machine learning models are only as good as the data they\u2019re trained on and the assumptions they make, and they can suffer from biases, errors, and uncertainties that can lead to incorrect or unfair decisions.\\n\\nMoreover, machine learning models can\u2019t explain how they arrived at their decisions or provide context and nuance that human experts can. Therefore, it\u2019s important to use them as a complement to human expertise and judgment, and to ensure that the models are transparent, explainable, and aligned with ethical and social values.\\n\\n## Myth #6: Big data is always better than small data\\n\\nAnother myth perpetuated by some proponents of data science is that big data is always better than small data. They claim that more data leads to more accurate and reliable models and insights, and that small data is insufficient and irrelevant for modern data science.\\n\\nThe reality is that while big data can provide more samples and variety of data, it can also introduce noise, redundancy, and complexity that can make analysis and modeling more difficult and less interpretable. Moreover, big data can pose ethical and privacy challenges, such as data ownership, consent, and security, that require careful consideration and management.\\n\\nTherefore, it\u2019s important to choose the appropriate data size and quality for the problem at hand, and to balance the trade-offs between data quantity and data quality, as well as the cost and benefit of collecting and processing more data.\\n\\n## Myth #7: Deep learning is the only type of machine learning\\n\\nDeep learning, a type of machine learning that involves neural networks with many layers, has gained a lot of attention and success in recent years, especially in domains such as image processing, speech recognition, and natural language processing. However, some people believe that deep learning is the only type of machine learning and that it\u2019s the ultimate solution to all machine learning problems.\\n\\nThe reality is that deep learning is only one type of machine learning, and it\u2019s not always the most suitable or efficient one. Other types of machine learning, such as random forests, support vector machines, and logistic regression, have their own strengths and weaknesses and can be more appropriate for different types of problems and data.\\n\\nMoreover, deep learning requires a lot of data, computation, and expertise to train and tune, and it can suffer from overfitting, generalization, and interpretability issues. So, it\u2019s important to choose the appropriate machine learning algorithm and architecture based on the problem at hand, the data characteristics, and the available resources and expertise.\\n\\n## Conclusion\\n\\nGet-rich-quick schemes in data science are based on false promises, misleading claims, and dangerous myths. They exploit people\u2019s aspirations and fears, and they undermine the integrity and credibility of data science as a profession and a discipline. As aspiring data scientists, we should reject these schemes and instead embrace the principles of honesty, curiosity, and responsibility, and strive to contribute to the advancement and the positive impact of data science on society."},{"id":"/guideline-ml","metadata":{"permalink":"/portfolio/blog/guideline-ml","source":"@site/blog/guideline-ml.md","title":"Advanced Guidelines for ML Model Training","description":"Understanding Model Training","date":"2024-04-09T10:32:08.000Z","formattedDate":"April 9, 2024","tags":[{"label":"Machine Learning","permalink":"/portfolio/blog/tags/machine-learning"},{"label":"Model Training","permalink":"/portfolio/blog/tags/model-training"},{"label":"Guidelines","permalink":"/portfolio/blog/tags/guidelines"},{"label":"Data Science","permalink":"/portfolio/blog/tags/data-science"},{"label":"Neural Networks","permalink":"/portfolio/blog/tags/neural-networks"}],"readingTime":12.375,"hasTruncateMarker":true,"authors":[{"name":"Morteza Hosseini","title":"Data scientist / ML engineer","key":"mori"}],"frontMatter":{"title":"Advanced Guidelines for ML Model Training","authors":["mori"],"tags":["Machine Learning","Model Training","Guidelines","Data Science","Neural Networks"]},"unlisted":false,"prevItem":{"title":"Get-Rich-Quick Schemes are a Bad Idea","permalink":"/portfolio/blog/get-rich-quick"},"nextItem":{"title":"Introducing Keras Core","permalink":"/portfolio/blog/keras-core"}},"content":"## Understanding Model Training\\n\\nWelcome to the captivating realm of machine learning, where algorithms breathe life into data and unveil patterns that were once hidden in the shadows. Before we dive into the intricate dance of code and data, let\u2019s take a moment to understand the essence of model training.\\n\\nImagine yourself as an artisan, crafting a masterpiece from raw materials. Just as a painter starts with a blank canvas, you begin with a dataset rich in information. This dataset is your palette, and your model is the brush that will paint the future. \ud83c\udfa8\ud83e\udd16\\n\\nModel training is the process of imbuing your creation with the ability to learn from data and make predictions. Just as a symphony conductor guides each musician to play in harmony, you guide your model through the data.\\n\\n\x3c!--truncate--\x3e\\n\\n## Preparing your data\\n\\nIn the grand symphony of model training, data preprocessing plays the role of tuning each instrument before the performance. Just as a musician wouldn\u2019t play with a broken string, your model won\u2019t perform well with unrefined data. \ud83c\udfbb\ud83c\udfbc\\n\\nLet\u2019s embark on the journey of transforming raw data into a harmonious dataset:\\n\\n- **Data Cleaning:** Begin by scrubbing your data clean. Detect and handle _missing values_, _outliers_, and inconsistencies that could disrupt your model\u2019s rhythm.\\n- **Feature Engineering:** Craft a melody of features that resonate with your model\u2019s _objective_. Engineer meaningful features that capture the essence of your problem domain.\\n- **Scaling and Normalization:** Ensure that your features are on the same scale, allowing your model to converge faster and avoid undue influence from dominant features.\\n- **Encoding:** Convert _categorical_ variables into a format that the model can understand. Whether it\u2019s _one-hot encoding_, _label encoding_, or other techniques, create a harmonious blend of categorical and numerical features.\\n- **Train-Validation-Test Split:** Divide your dataset into training, validation, and test sets. Each has a unique role in refining your model\u2019s performance.\\n- **Data Augmentation:** For image and sequence data, explore the realm of data augmentation. This technique introduces variety into your training data, enabling your model to _generalize better_.\\n\\n## Writing Efficient Training Loops for Neural Networks\\n\\nNow that your dataset is preprocessed, it\u2019s time to step into the realm of code and write the enchanting spells that will bring your model to life. Just as a playwright crafts a compelling script, you\u2019ll code a training loop that guides your model\u2019s evolution:\\n\\n- **Initialization:** Set the stage by initializing your model\u2019s architecture, loss function, and optimizer. Think of this as creating the canvas upon which your model will paint its predictions.\\n- **Epoch:** Enter the world of epochs, where each iteration weaves a new chapter of learning. Design your training loop to iterate through your dataset, presenting patterns to your model that it can decipher.\\n- **Batch:** Instead of presenting your model with the entire dataset at once, serve it bite-sized portions called batches. This _accelerates learning_ and _reduces memory consumption_.\\n- **Backpropagation:** In this step, your model learns from its mistakes by adjusting its internal parameters. Gradient descent guides it closer to accurate predictions with each step.\\n- **Validation:** Don\u2019t forget your validation set! After each epoch, serenade your model with the validation data to gauge its performance and _prevent overfitting_.\\n- **Early Stopping:** A magical trick to avoid prolonged training. If your model\u2019s performance on the validation set stagnates or worsens, elegantly halt the training and _save resources_.\\n- **Checkpoint:** Cast a checkpoint spell to save your model\u2019s progress during training. If anything goes awry, you can always _pick up where you left off_.\\n\\nAs you craft your training loop, envision yourself as a conductor guiding your model towards mastery. Each line of code is a note in the symphony of learning, harmonizing to create a melody of intelligence. Just as a conductor brings out the best from each musician, you\u2019ll coax out the finest predictions from your model, one iteration at a time. \ud83c\udfb5\ud83d\udd25\\n\\n## Optimizing Hyperparameters\\n\\nAs you embark on the journey of machine learning, you\u2019ll encounter hyperparameters&mdash;settings that wield immense influence over your model\u2019s performance. Much like a blacksmith forging a mighty sword, you\u2019ll shape these hyperparameters to enhance your model\u2019s power:\\n\\n- **Hyperspace:** Dive into the realm of hyperparameters&mdash;learning rate, batch size, hidden units, and more. Each choice alters your model\u2019s trajectory, and finding the right balance is your quest.\\n- **Grid Search:** Equip yourself with a grid search spell to systematically explore different hyperparameter combinations. Uncover the sweet spot that _maximizes_ your model\u2019s performance.\\n- **Random Search:** Embark on an adventure that balances exploration and efficiency. Randomly sample hyperparameter values to discover hidden gems without exhaustive searching.\\n- **Bayesian Optimization:** Unveil the elegance of Bayesian optimization&mdash;a smarter way to search for optimal hyperparameters. This method intelligently _narrows down the search space_ and accelerates your quest.\\n- **Regularization:** Beware the curse of overfitting on your hyperparameters. Just as knights wear armor for protection, regularize your hyperparameters to prevent them from _fitting noise_.\\n- **Automated Tuning:** Employ automated hyperparameter tuning libraries like [Optuna](https://optuna.org/) or [Hyperopt](https://hyperopt.github.io/hyperopt/). These tools streamline the search for optimal hyperparameters, freeing you to focus on your model\u2019s magic.\\n\\nIn your pursuit of optimal hyperparameters, imagine yourself as an alchemist blending rare ingredients. Each hyperparameter value is a crucial component, and the right blend will transform your model from good to extraordinary. Just as an alchemist\u2019s concoction holds secrets, your choice of hyperparameters will unlock the hidden potential within your model. \ud83e\uddea\u2728\\n\\n## Regularization Techniques\\n\\nIn the intricate tapestry of machine learning, complexity can sometimes lead to chaos. Regularization techniques are your spells to tame the unruly, ensuring that your model doesn\u2019t succumb to _overfitting_. Just as a skilled trainer guides a wild beast, you\u2019ll control the complexity of your model:\\n\\n- **Lasso and Ridge:** Explore the wonders of L1 and L2 regularization. These techniques add a touch of magic to your loss function, _penalizing overly complex models_ and ushering in simplicity.\\n- **Dropout:** Unveil the enigmatic power of dropout&mdash;a technique where neurons \u201cdisappear\u201d during training. This prevents _co-dependency_ and encourages each neuron to be self-sufficient.\\n- **Batch Normalization:** Cast a charm that stabilizes learning by normalizing the inputs of each layer. This technique keeps your model from wandering off course as it learns.\\n- **Early Stopping:** Revisit the early stopping charm from the training section. Here, it serves as a powerful regularization potion, halting training before your model becomes a slave to noise.\\n- **Data Augmentation:** Recall the data augmentation magic from data preprocessing. It\u2019s not just about variety; it also serves as a subtle form of regularization that enhances your model\u2019s ability to generalize.\\n- **Regularization:** Utilize validation data to decide which regularization techniques to employ. Just as a wise oracle provides guidance, your validation set reveals the best path to controlling complexity.\\n\\nAs you navigate the realm of regularization, think of yourself as a seasoned mage maintaining the delicate balance between power and control. Each technique you employ is a magical restraint, ensuring that your model\u2019s capabilities are channeled in the right direction. Just as a skilled magician can awe with a controlled display of magic, your model will captivate with its accuracy and generalization. \ud83c\udfa9\ud83d\udd2e\\n\\n## Choosing the Right Loss Functions\\n\\nIn the landscape of machine learning, loss functions are the compass that guides your model toward mastery. Just as a skilled navigator charts a course through uncharted waters, you\u2019ll choose the perfect loss function to steer your model towards accurate predictions:\\n\\n- **Mean Squared Error:** Begin with the classic melody of mean squared error. This loss function quantifies the distance between predictions and actual values, guiding your model toward precision.\\n- **Cross-Entropy:** This loss function elegantly captures the divergence between predicted and actual probabilities, ensuring your model learns with finesse.\\n- **Huber Loss:** A blend of mean squared error and mean absolute error, it\u2019s _robust against outliers_, helping your model navigate rough waters.\\n- **Custom Loss:** Compose your own loss function to address the unique nuances of your problem. Just as a composer tailors music to evoke specific emotions, you\u2019ll tailor your loss function to elicit accurate predictions.\\n- **Weighted Loss:** Tune the weights of your loss function to emphasize certain samples. This technique is like adjusting the volume of different instruments to achieve a balanced composition.\\n\\nAs you explore the symphony of loss functions, envision yourself as a maestro directing your model\u2019s learning journey. Each loss function is a unique melody that guides your model through the intricacies of the data landscape. Just as a maestro extracts the best from each instrument, you\u2019ll coax your model to produce predictions that resonate with accuracy. \ud83c\udfb6\ud83c\udfa4\\n\\n## Crafting Custom Neural Architectures\\n\\nAs you venture deeper into the realm of machine learning, you\u2019ll realize that models are like sculptures waiting to take shape. Default architectures might not always fit your vision perfectly. It\u2019s time to don the mantle of a sculptor and craft custom neural architectures that bring your imagination to life:\\n\\n- **The Blank Canvas:** Begin with a clear mind. Analyze your problem\u2019s nuances, data characteristics, and objectives. This blank canvas is where your creative journey starts.\\n- **Architectural Elements:** Choose the building blocks for your masterpiece. From convolutional layers for images to recurrent layers for sequences, each element has a unique role in your design.\\n- **Skip Connections:** Embrace skip connections&mdash;bridges that enable information to flow across different layers. Like secret passages in a castle, they enhance gradient flow and promote efficient learning.\\n- **Depth and Width:** Determine the depth and width of your architecture. Deeper networks capture intricate details, while wider networks handle complex relationships. Strike the right balance for your task.\\n- **Residual Networks:** Integrate residual networks (ResNets) for _smoother gradient propagation_. These magical shortcuts encourage your model to focus on learning the residual information.\\n- **Attention:** Infuse attention mechanisms to allow your model to focus on relevant parts of the input. This technique is like a spotlight that illuminates the most important features.\\n- **Reinforcement Learning:** Dive into the exciting world of neural architecture search. Just as explorers discover new lands, you\u2019ll use reinforcement learning to unearth optimal architectures.\\n\\nImagine yourself as an architect designing a grand structure. Your neural architecture is the blueprint, each layer a carefully chosen element contributing to the final form. Just as an architect molds space to evoke emotions, you\u2019ll shape your model to extract insights from data and make predictions that resonate with accuracy. \ud83c\udfdb\ufe0f\ud83d\udd25\\n\\n## The Power of Combining Models\\n\\nIn the enchanted forest of machine learning, individual models are like trees&mdash;strong, but together, they form a majestic forest. Ensembles are your magical incantations, summoning the power of multiple models to create a force that\u2019s greater than the sum of its parts:\\n\\n- **Bagging:** Bootstrap Aggregating combines predictions from diverse models, smoothing out errors and enhancing generalization.\\n- **Boosting:** Algorithms like [AdaBoost](https://en.wikipedia.org/wiki/AdaBoost) and [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) mold weak learners into a formidable force, focusing on areas where previous models stumbled.\\n- **Stacking:** Stacking combines predictions from various models, creating a meta-model that learns to weigh their expertise.\\n- **Voting Ensemble:** Gather your models for a voting ensemble. Each model casts a vote, and the most popular prediction emerges victorious. It\u2019s like the collective wisdom of a council.\\n- **Ensemble Hyperparameters:** Tune the hyperparameters of your ensemble.\\n- **Ensemble Diversity:** Embrace diversity among your models. Different architectures, algorithms, and training strategies create a symphony of perspectives that boost ensemble performance.\\n\\nEnsemble techniques are your orchestra, playing in harmony to create predictions that surpass the capabilities of any individual model. Envision yourself as a conductor orchestrating a magnificent performance, where each model contributes its unique melody to create a harmonious ensemble. Just as an ensemble elevates a performance to new heights, your ensemble of models will elevate your predictions to levels of unparalleled accuracy. \ud83c\udfbb\ud83c\udfb9\\n\\n## Leveraging Callbacks for Training Insights\\n\\nIn the labyrinth of machine learning, every training iteration is a step towards mastery. Callbacks are the lanterns that illuminate this path, providing insights and guidance throughout the training journey. Imagine yourself as an explorer equipped with these magical tools, unraveling the secrets of your model\u2019s learning process:\\n\\n- **Early Stopping:** Summon the early stopping whisperer, a callback that listens to your model\u2019s performance. It knows when to _halt training before overfitting_ casts its shadow.\\n- **Learning Rate Scheduler:** Cast a spell to adjust the learning rate as your model learns. This dynamic tuning prevents overshooting and helps converge to the optimal point.\\n- **Model Checkpoint:** Enchant your training loop with a model checkpoint charm. It saves the model\u2019s progress at intervals, ensuring you never lose the map of your journey.\\n- **Custom Callback:** Craft your own custom callbacks to fit your unique needs. Whether it\u2019s monitoring specific metrics or injecting special techniques, these callbacks are your tailored assistants.\\n\\nAs you explore the realm of callbacks, imagine yourself as a wise sage, attuned to the whispers of your model\u2019s learning journey. Each callback is a guide that imparts insights, helping you make informed decisions at every turn. Just as a sage interprets the signs of nature, you\u2019ll interpret the callbacks\u2019 cues to steer your model towards excellence. \ud83d\udd6f\ufe0f\ud83d\udd0d\\n\\n## Staying Updated with Research and Trends\\n\\nIn the ever-evolving realm of machine learning, staying stagnant is not an option. Trends shift, algorithms evolve, and new techniques emerge like shooting stars across the night sky. You are the guardian of your craft, and mastering momentum is your key to staying at the forefront:\\n\\n- **Lifelong Learning:** Take a lifelong learning oath. Just as a master craftsman hones their skills over time, commit to a journey of continuous improvement in this dynamic field.\\n- **Research Paper:** Embark on a quest to unravel research papers. Dive into the wealth of knowledge shared by the community, absorbing the latest breakthroughs and techniques.\\n- **Online Courses:** Enroll in online courses to refine your skills. These are your magical academies, offering structured lessons to help you master new technologies and methods.\\n- **Tech Conference:** Attend tech conferences and symposiums. Like a traveler exploring distant lands, immerse yourself in the sea of ideas, networking, and hands-on experience.\\n- **Blogosphere:** Contribute to the blogosphere by sharing your own insights and experiences. Just as a bard shares stories, you\u2019ll contribute to the collective knowledge of the community.\\n- **Collaboration:** Collaborate with fellow wizards&mdash;engage in discussions, exchange ideas, and collaborate on projects. The synergy of minds is your catalyst for growth.\\n- **Model Zoo Exploration:** Explore pre-trained models and libraries. These treasure troves of pre-built models and functions are like enchanted artifacts that save time and effort.\\n\\nImagine yourself as a celestial navigator, steering your ship through the cosmic sea of knowledge. Each trend, breakthrough, and technique is a star guiding your way. Just as a navigator charts new territories, you\u2019ll explore the uncharted horizons of machine learning, always seeking to harness the latest innovations and propel your craft forward. \ud83d\ude80\u2728"},{"id":"/keras-core","metadata":{"permalink":"/portfolio/blog/keras-core","source":"@site/blog/keras-core.md","title":"Introducing Keras Core","description":"Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.","date":"2024-04-09T10:32:08.000Z","formattedDate":"April 9, 2024","tags":[{"label":"Keras","permalink":"/portfolio/blog/tags/keras"},{"label":"PyTorch","permalink":"/portfolio/blog/tags/py-torch"},{"label":"Jax","permalink":"/portfolio/blog/tags/jax"},{"label":"Deep Learning","permalink":"/portfolio/blog/tags/deep-learning"},{"label":"Machine Learning","permalink":"/portfolio/blog/tags/machine-learning"}],"readingTime":4.32,"hasTruncateMarker":true,"authors":[{"name":"Morteza Hosseini","title":"Data scientist / ML engineer","key":"mori"}],"frontMatter":{"title":"Introducing Keras Core","authors":["mori"],"tags":["Keras","PyTorch","Jax","Deep Learning","Machine Learning"]},"unlisted":false,"prevItem":{"title":"Advanced Guidelines for ML Model Training","permalink":"/portfolio/blog/guideline-ml"},"nextItem":{"title":"Why is Parquet format so popular?","permalink":"/portfolio/blog/parquet"}},"content":"Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.\\n\\nExciting times lie ahead!\\n\\n## Why Use Keras Core?\\n\\nBut wait, why are they making Keras multi-backend again? Let\u2019s take a quick trip down memory lane. Not too long ago, Keras had the ability to run on multiple backends like Theano, TensorFlow, CNTK, and even MXNet. However, in 2018, they decided to focus exclusively on TensorFlow as other backends discontinued development. But times have changed! Fast forward to 2023, and we see TensorFlow dominating the production ML space with a market share of 55% to 60%. On the other hand, PyTorch has captured the ML research realm with a market share of 40% to 45%. Meanwhile, JAX, although with a smaller market share, has gained recognition from leading players in generative AI. It\u2019s clear that each framework has its strengths and user base. Keras Core enables the users to leverage the power of all three frameworks simultaneously.\\n\\nSay goodbye to framework silos and welcome the new era of multi-framework ML!\\n\\n\x3c!--truncate--\x3e\\n\\n## Exploring the Main Features of Keras Core\\n\\nNow that we understand the motivation behind Keras Core, let\u2019s dive into its incredible features.\\n\\n- **Always get the best performance for your models:** With Keras Core, you can achieve optimal training and inference performance across different hardware platforms. The benchmarks indicate that JAX usually delivers the best performance on GPU, TPU, and CPU. However, results may vary depending on the model, and non-XLA TensorFlow occasionally outperforms on GPU. The beauty of Keras Core lies in its ability to dynamically select the backend that will offer the highest efficiency for your specific model. No code changes required!\\n- **Maximize available ecosystem surface for your models:** Keras Core opens up a world of possibilities by allowing you to utilize your models with various ecosystems. Any Keras Core model can be used as a PyTorch `Module`, exported as a TensorFlow `SavedModel`, or instantiated as a stateless JAX function. This flexibility means you can leverage PyTorch ecosystem packages, TensorFlow deployment and production tools (such as TF-Serving, TF.js, and TFLite), and JAX\u2019s large-scale TPU training infrastructure. Write your `model.py` using Keras Core APIs, and unlock access to everything the ML world has to offer.\\n- **Maximize distribution for your open-source model releases:** If you want to release a pretrained model and make it accessible to as many people as possible, Keras Core is your secret weapon. By implementing your model in Keras Core, you instantly make it usable by anyone, regardless of their framework preference. While implementing in pure TensorFlow or PyTorch limits your reach to roughly half the market, Keras Core doubles your impact at no additional development cost. Get ready to leave a lasting impression!\\n- **Use data pipelines from any source:** The versatility of Keras Core shines when it comes to data pipelines. Whether you\u2019re working with `tf.data.Dataset` objects, PyTorch `DataLoader` objects, NumPy arrays, or Pandas dataframes, the Keras Core `fit()`, `evaluate()`, and `predict()` routines seamlessly integrate with all backends. Train a Keras Core + TensorFlow model on a PyTorch `DataLoader` or a Keras Core + PyTorch model on a `tf.data.Dataset`. The choice is yours, and Keras Core adapts effortlessly.\\n\\n## Empowering Developers with Progressive Disclosure and a Stateless API\\n\\nThe Keras team have brought the principle of progressive disclosure of complexity to Keras Core, just like in the existing Keras API. You can start with simple workflows using `Sequential` and `Functional` models, training them with `fit()`. As your needs grow, you can easily customize different components while reusing most of your existing code. Keras Core allows you to maintain a smooth transition without hitting a complexity cliff or requiring a switch to a different set of tools. For instance, you can customize your training loop while leveraging the power of `fit()` by simply overriding the `train_step` method. The possibilities are endless!\\n\\nBut that\u2019s not all. Keras Core introduces a new stateless API for layers, models, metrics, and optimizers, catering to functional programming enthusiasts. All stateful objects in Keras now have a stateless API, making them compatible with JAX functions that require full statelessness. Layers and models, optimizers, and metrics provide `stateless_call()`, `stateless_apply()`, `stateless_update_state()` and `stateless_result()` methods, respectively. These methods offer the same functionalities as their stateful counterparts but without any side effects. You can effortlessly use them in your JAX, PyTorch, or TensorFlow workflows, ensuring a smooth and enjoyable development experience.\\n\\n## The Bottom line\\n\\nIt\u2019s time to embrace the future of deep learning frameworks with Keras Core. Unlock the potential of multi-framework ML, achieve top performance, maximize ecosystem compatibility, and enjoy the flexibility of the stateless API. Say goodbye to limitations and hello to boundless possibilities!\\n\\nHappy coding with Keras Core and may your machine learning endeavors reach new heights!"},{"id":"/parquet","metadata":{"permalink":"/portfolio/blog/parquet","source":"@site/blog/parquet.md","title":"Why is Parquet format so popular?","description":"Introduction","date":"2024-04-09T10:32:08.000Z","formattedDate":"April 9, 2024","tags":[{"label":"Data Science","permalink":"/portfolio/blog/tags/data-science"},{"label":"Parquet","permalink":"/portfolio/blog/tags/parquet"},{"label":"Data Storage","permalink":"/portfolio/blog/tags/data-storage"},{"label":"Spark","permalink":"/portfolio/blog/tags/spark"},{"label":"Pandas Dataframe","permalink":"/portfolio/blog/tags/pandas-dataframe"}],"readingTime":3.92,"hasTruncateMarker":true,"authors":[{"name":"Morteza Hosseini","title":"Data scientist / ML engineer","key":"mori"}],"frontMatter":{"title":"Why is Parquet format so popular?","authors":["mori"],"tags":["Data Science","Parquet","Data Storage","Spark","Pandas Dataframe"]},"unlisted":false,"prevItem":{"title":"Introducing Keras Core","permalink":"/portfolio/blog/keras-core"},"nextItem":{"title":"Venn Diagrams: Art & Pitfalls","permalink":"/portfolio/blog/venn"}},"content":"## Introduction\\n\\n[Parquet](https://parquet.apache.org/) is a popular columnar storage format for big data processing. It\u2019s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we\u2019ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we\u2019ll compare it to the popular pandas DataFrame.\\n\\n\x3c!--truncate--\x3e\\n\\n## History\\n\\nThe Parquet format was created in 2013 by the Apache Software Foundation\u2019s Parquet project, a collaboration between Twitter, Cloudera, and other organizations. The goal of the project was to create a columnar storage format that was optimized for big data processing and could be used with a variety of data processing frameworks such as Hadoop, Impala, and Hive. The project was a response to the growing need for a more efficient way of storing and processing large datasets as data collection and storage was rapidly increasing. Since its release, the Parquet format has become one of the most popular storage formats for big data, widely used in the industry and adopted by many companies.\\n\\n## Why so popular?\\n\\nThe first reason why Parquet is so popular is its high compression and encoding capabilities. Parquet uses a technique called columnar storage, which organizes data in a way that allows for more efficient compression and encoding. This means that data stored in Parquet format takes up less space on disk and can be read and processed more quickly.\\n\\nAnother benefit of Parquet is its support for advanced data types and encoding schemes. Parquet supports a wide range of data types, including integers, floating-point numbers, strings, and timestamps. It also supports advanced encoding schemes like dictionary encoding, which can further reduce the size of the data on disk.\\n\\nParquet also supports advanced data querying capabilities. It provides a feature called predicate pushdown, which allows query engines to filter data on disk before reading it into memory. This reduces the amount of data that needs to be read and processed, and can greatly improve query performance.\\n\\nIn addition to its high compression and encoding capabilities, Parquet is also highly interoperable. It\u2019s supported by a wide range of big data processing tools and platforms, including Hadoop, Spark, Hive, and Pig. This means that data stored in Parquet format can be easily read and processed by a wide range of tools and platforms, making it a versatile format for big data processing tasks.\\n\\nFinally, Parquet is an open-source format, which means that it can be freely used and modified by anyone. This has led to a large and active community of developers working on the format, which has helped to improve its performance and capabilities over time.\\n\\n## Parquet vs pandas DataFrame\\n\\n| Feature             | Parquet                                | pandas DataFrames                       |\\n| ------------------- | -------------------------------------- | --------------------------------------- |\\n| Storage format      | Columnar                               | Row-based                               |\\n| Compression         | High                                   | Low                                     |\\n| Encoding            | Advanced (dictionary, RLE, etc.)       | Basic                                   |\\n| Data querying       | Advanced (predicate pushdown)          | Basic                                   |\\n| Handling large data | Efficiently handle large data          | Not optimized for large data processing |\\n| Execution           | Can be executed in distributed systems | Executed on single machine              |\\n| Open-source         | Yes                                    | Yes                                     |\\n\\nAs you can see, Parquet and [pandas](https://pandas.pydata.org/) DataFrames have some similarities, but they also have some important differences. Parquet is a columnar storage format that is optimized for big data processing and provides advanced compression and encoding capabilities. On the other hand, pandas DataFrames are row-based and are more commonly used for data analysis and manipulation.\\n\\nOne of the main benefits of using Parquet is that it can greatly improve the performance and efficiency of big data processing tasks by reducing the amount of data that needs to be read and processed. pandas DataFrames, on the other hand, are not as optimized for big data processing and may not be as efficient when working with large datasets.\\n\\n![](img/parquet-row-col.jpeg)\\nhttps://timepasstechies.com/row-oriented-column-oriented-file-formats-hadoop/\\n\\nAnother benefit of Parquet is that it supports a wide range of data types and encoding schemes, which can further reduce the size of the data on disk. pandas DataFrames, on the other hand, have a limited set of data types and do not support advanced encoding schemes.\\n\\n## Conclusion\\n\\nParquet is a popular columnar storage format for big data processing due to its high compression and encoding capabilities, support for advanced data types and encoding schemes, advanced data querying capabilities, high interoperability, and the fact that it is open-source. These benefits make it a powerful and versatile format for big data processing tasks, and it\u2019s likely to continue to be widely used in the future."},{"id":"/venn","metadata":{"permalink":"/portfolio/blog/venn","source":"@site/blog/venn.md","title":"Venn Diagrams: Art & Pitfalls","description":"In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I\u2019ve encountered their allure and their limitations. In this article, we\u2019ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We\u2019ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.","date":"2024-04-09T10:32:08.000Z","formattedDate":"April 9, 2024","tags":[{"label":"Data Science","permalink":"/portfolio/blog/tags/data-science"},{"label":"Data Visualization","permalink":"/portfolio/blog/tags/data-visualization"},{"label":"Venn Diagrams","permalink":"/portfolio/blog/tags/venn-diagrams"},{"label":"Pitfalls","permalink":"/portfolio/blog/tags/pitfalls"},{"label":"Misinterpretations","permalink":"/portfolio/blog/tags/misinterpretations"}],"readingTime":4.55,"hasTruncateMarker":true,"authors":[{"name":"Morteza Hosseini","title":"Data scientist / ML engineer","key":"mori"}],"frontMatter":{"title":"Venn Diagrams: Art & Pitfalls","authors":["mori"],"tags":["Data Science","Data Visualization","Venn Diagrams","Pitfalls","Misinterpretations"]},"unlisted":false,"prevItem":{"title":"Why is Parquet format so popular?","permalink":"/portfolio/blog/parquet"}},"content":"In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I\u2019ve encountered their allure and their limitations. In this article, we\u2019ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We\u2019ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.\\n\\n\x3c!--truncate--\x3e\\n\\nThe followings are some reasons why Venn diagrams can sometimes be considered misleading.\\n\\n## Misrepresentation of Set Sizes\\n\\nVenn diagrams, often characterized by their overlapping circles, can inadvertently create a false impression of the sizes of the sets they represent. The issue arises from the inherent limitations of Venn diagrams, where the circles\u2019 sizes are typically not proportional to the actual cardinalities of the sets. This can lead to a misperception of the data, as viewers may wrongly assume that each set is of equal size or that the proportions between sets differ from reality. Such misrepresentations can have far-reaching consequences, impacting decision-making processes and resource allocation strategies. It underscores the importance of using Venn diagrams judiciously and considering alternative visualization methods when precision in conveying set sizes is paramount in data analysis.\\n\\n![Wikimedia](img/venn-missrep-set-size.jpeg)\\n\\n## Limited to Two or Three Sets\\n\\nOne of the inherent constraints of Venn diagrams is their limited applicability to visualizing relationships between only two or three sets effectively. While these diagrams excel at illustrating the intersections and overlaps between a small number of sets, they become less practical when dealing with more complex data scenarios involving multiple overlapping sets. Attempting to represent numerous sets with overlapping circles in a Venn diagram can result in a convoluted and challenging-to-interpret visualization. This limitation highlights the need for data analysts and scientists to consider alternative visualization techniques, such as Euler diagrams, when dealing with larger and more intricate datasets with numerous intersecting subsets.\\n\\n![Wikimedia](img/venn-limited.jpeg)\\n\\n## Overlapping Interpretation\\n\\nThe interpretation of overlapping regions in Venn diagrams can often be a source of confusion and misinterpretation. While these overlapping areas are intended to represent the elements shared between sets, the extent and significance of this overlap may not be immediately clear. Viewers might struggle to discern the precise number of elements in these intersections, making it challenging to draw accurate conclusions. Additionally, it may not always be apparent which sets contribute more or less to the overlap, leaving room for ambiguity in understanding the relationships between sets. This ambiguity can be particularly problematic in complex data scenarios with multiple sets, where the nuances of overlapping interpretations become even more pronounced. To address this challenge, it\u2019s essential to provide clear labels, context, and supplementary information when using Venn diagrams to ensure that viewers can accurately decipher the overlapping regions and their implications within the data.\\n\\n![](img/venn-overlap.jpeg)\\n\\n## Non-binary Data\\n\\nVenn diagrams are most effective when dealing with binary data, where elements either belong to a set or do not. However, they can become less suitable when confronted with non-binary data, which encompasses more nuanced membership or gradations of inclusion. In situations where elements can belong to multiple sets simultaneously or have varying degrees of membership, Venn diagrams may oversimplify the complexity of these relationships. Attempting to represent such data in a binary, all-or-nothing fashion within the confines of overlapping circles can lead to a loss of valuable information and potentially mislead viewers. For these scenarios, alternative visualization methods like heatmaps or network graphs may offer a more accurate depiction of the intricate interconnections and degrees of membership present in the dataset.\\n\\n## Contextual Misuse\\n\\nContextual misuse of Venn diagrams can occur when these visual tools are applied in inappropriate situations or for purposes they were not designed for. While Venn diagrams excel at representing set relationships, they may fall short when used to convey other types of data, such as numerical values or probabilistic information. Employing Venn diagrams in the wrong context can lead to misinterpretations, confusion, or a lack of clarity in conveying the intended message. It\u2019s crucial for data scientists and analysts to exercise discretion and choose the most suitable visualization method based on the nature of the data they are dealing with. By recognizing the limitations of Venn diagrams and reserving them for scenarios where they can provide meaningful insights into set relationships, we can avoid the pitfalls of contextual misuse and ensure that data visualizations effectively communicate the desired information.\\n\\n## Lack of Quantitative Information\\n\\nA notable limitation of Venn diagrams is their inherent lack of quantitative information. These diagrams are primarily designed to show the relationships and intersections between sets visually, but they do not provide numerical or quantitative data. Consequently, when relying solely on Venn diagrams, it can be challenging to ascertain the exact size of sets, compare set sizes accurately, or determine the precise proportions of elements within overlapping regions. This absence of quantitative information can limit the depth of analysis and decision-making based on Venn diagrams alone. To address this limitation, it is advisable to complement Venn diagrams with numerical data or use other types of visualizations, such as bar charts or histograms, to provide viewers with a more comprehensive and precise understanding of the dataset.\\n\\n![](img/venn-lack-quant-info.jpeg)"}]}')}}]);
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Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."}),"\n",(0,t.jsx)(o.p,{children:"Exciting times lie ahead!"}),"\n",(0,t.jsx)(o.h2,{id:"why-use-keras-core",children:"Why Use Keras Core?"}),"\n",(0,t.jsx)(o.p,{children:"But wait, why are they making Keras multi-backend again? Let\u2019s take a quick trip down memory lane. Not too long ago, Keras had the ability to run on multiple backends like Theano, TensorFlow, CNTK, and even MXNet. However, in 2018, they decided to focus exclusively on TensorFlow as other backends discontinued development. But times have changed! Fast forward to 2023, and we see TensorFlow dominating the production ML space with a market share of 55% to 60%. On the other hand, PyTorch has captured the ML research realm with a market share of 40% to 45%. Meanwhile, JAX, although with a smaller market share, has gained recognition from leading players in generative AI. It\u2019s clear that each framework has its strengths and user base. 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Write your ",(0,t.jsx)(o.code,{children:"model.py"})," using Keras Core APIs, and unlock access to everything the ML world has to offer."]}),"\n",(0,t.jsxs)(o.li,{children:[(0,t.jsx)(o.strong,{children:"Maximize distribution for your open-source model releases:"})," If you want to release a pretrained model and make it accessible to as many people as possible, Keras Core is your secret weapon. By implementing your model in Keras Core, you instantly make it usable by anyone, regardless of their framework preference. While implementing in pure TensorFlow or PyTorch limits your reach to roughly half the market, Keras Core doubles your impact at no additional development cost. Get ready to leave a lasting impression!"]}),"\n",(0,t.jsxs)(o.li,{children:[(0,t.jsx)(o.strong,{children:"Use data pipelines from any source:"})," The versatility of Keras Core shines when it comes to data pipelines. 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+"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[9130],{6501:(e,s,n)=>{n.r(s),n.d(s,{assets:()=>o,contentTitle:()=>r,default:()=>d,frontMatter:()=>t,metadata:()=>h,toc:()=>l});var i=n(85893),a=n(11151);const t={tags:["Hash","Data Structures","Algorithms","Tutorial","Python"]},r="A Guide to Hashing",h={id:"data-structure/hash",title:"A Guide to Hashing",description:"Introduction",source:"@site/docs/data-structure/hash.md",sourceDirName:"data-structure",slug:"/data-structure/hash",permalink:"/portfolio/docs/data-structure/hash",draft:!1,unlisted:!1,tags:[{label:"Hash",permalink:"/portfolio/docs/tags/hash"},{label:"Data Structures",permalink:"/portfolio/docs/tags/data-structures"},{label:"Algorithms",permalink:"/portfolio/docs/tags/algorithms"},{label:"Tutorial",permalink:"/portfolio/docs/tags/tutorial"},{label:"Python",permalink:"/portfolio/docs/tags/python"}],version:"current",frontMatter:{tags:["Hash","Data Structures","Algorithms","Tutorial","Python"]},sidebar:"tutorialSidebar",previous:{title:"Data Structures",permalink:"/portfolio/docs/data-structure/"},next:{title:"A Guide to Sorting",permalink:"/portfolio/docs/data-structure/sorting-popular"}},o={},l=[{value:"Introduction",id:"introduction",level:2},{value:"Why Do We Need Hashing?",id:"why-do-we-need-hashing",level:3},{value:"How Hashing Works",id:"how-hashing-works",level:3},{value:"Common Use Cases",id:"common-use-cases",level:3},{value:"Understanding Hash Functions",id:"understanding-hash-functions",level:2},{value:"What Is a Hash Function?",id:"what-is-a-hash-function",level:3},{value:"Commonly Used Hash Functions",id:"commonly-used-hash-functions",level:3},{value:"Common Use Cases for Hashing",id:"common-use-cases-for-hashing",level:2},{value:"Data Structures and Hash Tables",id:"data-structures-and-hash-tables",level:3},{value:"File Integrity Verification",id:"file-integrity-verification",level:3},{value:"Cryptography and Digital Signatures",id:"cryptography-and-digital-signatures",level:3},{value:"Content Deduplication",id:"content-deduplication",level:3},{value:"Implementing Hashing in Python",id:"implementing-hashing-in-python",level:2},{value:"Hashing Strings",id:"hashing-strings",level:3},{value:"Creating a Simple Hash Table",id:"creating-a-simple-hash-table",level:3},{value:"File Integrity Checks",id:"file-integrity-checks",level:3},{value:"Tips and Best Practices",id:"tips-and-best-practices",level:2},{value:"Choose the Right Hash Function",id:"choose-the-right-hash-function",level:3},{value:"Salting for Passwords",id:"salting-for-passwords",level:3},{value:"Avoid Hash Collisions",id:"avoid-hash-collisions",level:3},{value:"FAQs",id:"faqs",level:2}];function c(e){const s={code:"code",h1:"h1",h2:"h2",h3:"h3",li:"li",ol:"ol",p:"p",pre:"pre",strong:"strong",ul:"ul",...(0,a.a)(),...e.components};return(0,i.jsxs)(i.Fragment,{children:[(0,i.jsx)(s.h1,{id:"a-guide-to-hashing",children:"A Guide to Hashing"}),"\n",(0,i.jsx)(s.h2,{id:"introduction",children:"Introduction"}),"\n",(0,i.jsxs)(s.p,{children:["Hashing is a fundamental concept in computer science and plays a crucial role in various applications. At its core, hashing involves mapping data of arbitrary size (such as strings, files, or even entire databases) to fixed-size values. These fixed-size values are typically numeric and are known as ",(0,i.jsx)(s.strong,{children:"hash codes"})," or ",(0,i.jsx)(s.strong,{children:"hash values"}),"."]}),"\n",(0,i.jsx)(s.h3,{id:"why-do-we-need-hashing",children:"Why Do We Need Hashing?"}),"\n",(0,i.jsx)(s.p,{children:"Hashing provides several benefits:"}),"\n",(0,i.jsxs)(s.ol,{children:["\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Data Integrity"}),": Hashing ensures that data remains unchanged during transmission or storage. By comparing hash values before and after data transfer, we can verify whether the data has been altered."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Efficient Data Retrieval"}),": Hashing allows us to create data structures like hash tables, which enable fast retrieval of values based on keys. Think of hash tables as efficient dictionaries where keys are hashed to indices."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Security and Cryptography"}),": Hash functions are used in password storage, digital signatures, and cryptographic protocols. A good hash function should be resistant to collisions (two different inputs producing the same hash) and pre-image attacks (finding an input for a given hash)."]}),"\n"]}),"\n"]}),"\n",(0,i.jsx)(s.h3,{id:"how-hashing-works",children:"How Hashing Works"}),"\n",(0,i.jsxs)(s.ol,{children:["\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Hash Functions"}),": A hash function takes an input (often called a ",(0,i.jsx)(s.strong,{children:"message"}),") and produces a fixed-size output (the hash value). The output is typically a sequence of characters (hexadecimal digits). Examples of hash functions include MD5, SHA-1, and SHA-256."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Deterministic"}),": Hash functions are deterministic, meaning the same input will always produce the same hash value. This property is essential for consistency."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Fixed Output Size"}),": Regardless of input size, the hash function generates a fixed-length output. For instance, SHA-256 produces a 256-bit (32-byte) hash."]}),"\n"]}),"\n"]}),"\n",(0,i.jsx)(s.h3,{id:"common-use-cases",children:"Common Use Cases"}),"\n",(0,i.jsx)(s.p,{children:"Hashing is used in various scenarios:"}),"\n",(0,i.jsxs)(s.ul,{children:["\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Data Structures"}),": Hash tables, bloom filters, and caches rely on hashing for efficient data storage and retrieval."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"File Integrity"}),": Hashing verifies file integrity by comparing hash values before and after file transfer."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Cryptographic Applications"}),": Hash functions are used in digital signatures, password storage, and blockchain technology."]}),"\n"]}),"\n"]}),"\n",(0,i.jsx)(s.h2,{id:"understanding-hash-functions",children:"Understanding Hash Functions"}),"\n",(0,i.jsx)(s.p,{children:"In the previous section, we introduced the concept of hashing and its importance. Now, let's delve deeper into hash functions--the heart of hashing."}),"\n",(0,i.jsx)(s.h3,{id:"what-is-a-hash-function",children:"What Is a Hash Function?"}),"\n",(0,i.jsx)(s.p,{children:"A hash function is a mathematical algorithm that takes an input and produces a fixed-size output. The output is typically represented as a sequence of characters (usually hexadecimal digits). Here are some key characteristics of hash functions:"}),"\n",(0,i.jsxs)(s.ol,{children:["\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Deterministic"}),": A hash function is deterministic, meaning that given the same input, it will always produce the same hash value. This property ensures consistency and predictability."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Fixed Output Size"}),": Regardless of the input size, the hash function generates a fixed-length output. For instance, SHA-512 produces a 512-bit (64-byte) hash."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Avalanche Effect"}),": A small change in the input should result in a significantly different hash value. This property prevents attackers from easily predicting hash collisions."]}),"\n"]}),"\n"]}),"\n",(0,i.jsx)(s.h3,{id:"commonly-used-hash-functions",children:"Commonly Used Hash Functions"}),"\n",(0,i.jsx)(s.p,{children:"Several hash functions are widely used in practice:"}),"\n",(0,i.jsxs)(s.ol,{children:["\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"MD5 (Message Digest Algorithm 5)"}),": Although widely used in the past, MD5 is now considered insecure due to vulnerabilities. It produces a 128-bit hash."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"SHA-1 (Secure Hash Algorithm 1)"}),": Like MD5, SHA-1 is also deprecated due to vulnerabilities. It produces a 160-bit hash."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"SHA-256 (Secure Hash Algorithm 256)"}),": Part of the SHA-2 family, SHA-256 is widely used for cryptographic purposes. It produces a 256-bit hash and is considered secure."]}),"\n"]}),"\n"]}),"\n",(0,i.jsx)(s.h2,{id:"common-use-cases-for-hashing",children:"Common Use Cases for Hashing"}),"\n",(0,i.jsx)(s.p,{children:"Hashing finds applications in various domains, making it a versatile tool for data manipulation and security. Let's explore some common use cases:"}),"\n",(0,i.jsx)(s.h3,{id:"data-structures-and-hash-tables",children:"Data Structures and Hash Tables"}),"\n",(0,i.jsx)(s.p,{children:"Hash tables (or hash maps) are fundamental data structures that rely on hashing. They allow efficient storage and retrieval of key-value pairs. Here's how they work:"}),"\n",(0,i.jsxs)(s.ul,{children:["\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Hashing Keys"}),": When you insert a value into a hash table, a hash function computes an index (often called a ",(0,i.jsx)(s.strong,{children:"hash code"}),") based on the key. This index determines the position where the value is stored."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Fast Retrieval"}),": Hash tables provide constant-time average complexity for insertion, deletion, and retrieval. They are widely used in databases, caches, and language dictionaries."]}),"\n"]}),"\n"]}),"\n",(0,i.jsx)(s.h3,{id:"file-integrity-verification",children:"File Integrity Verification"}),"\n",(0,i.jsx)(s.p,{children:"Hashing ensures the integrity of files during transmission or storage. Here's how it works:"}),"\n",(0,i.jsxs)(s.ul,{children:["\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Checksums"}),": Before transferring a file, compute its hash value (checksum) using a hash function (e.g., SHA-256). Share this checksum with the recipient."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Verification"}),": After receiving the file, the recipient computes the hash value of the received file. If the computed hash matches the shared checksum, the file hasn't been tampered with."]}),"\n"]}),"\n"]}),"\n",(0,i.jsx)(s.h3,{id:"cryptography-and-digital-signatures",children:"Cryptography and Digital Signatures"}),"\n",(0,i.jsx)(s.p,{children:"Hash functions play a critical role in cryptography:"}),"\n",(0,i.jsxs)(s.ul,{children:["\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Password Storage"}),": Instead of storing plain-text passwords, systems store their hash values. When a user logs in, the system hashes the entered password and compares it with the stored hash."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Digital Signatures"}),": Hash functions are used to create digital signatures. A signer hashes the message and encrypts the hash with their private key. Recipients verify the signature by decrypting the hash with the signer's public key."]}),"\n"]}),"\n"]}),"\n",(0,i.jsx)(s.h3,{id:"content-deduplication",children:"Content Deduplication"}),"\n",(0,i.jsx)(s.p,{children:"Hashing helps identify duplicate content efficiently:"}),"\n",(0,i.jsxs)(s.ul,{children:["\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Content Addressable Storage (CAS)"}),": Systems store unique content (files, chunks, or blocks) based on their hash values. Duplicate content is automatically deduplicated."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Git Repositories"}),": Git uses SHA-256 hashes to uniquely identify commits, files, and objects. This ensures data integrity and efficient storage."]}),"\n"]}),"\n"]}),"\n",(0,i.jsx)(s.h2,{id:"implementing-hashing-in-python",children:"Implementing Hashing in Python"}),"\n",(0,i.jsx)(s.p,{children:"Now that we've covered the theory behind hash functions, let's roll up our sleeves and implement hashing in Python. We'll explore how to compute hash values, create hash tables, and handle common scenarios."}),"\n",(0,i.jsx)(s.h3,{id:"hashing-strings",children:"Hashing Strings"}),"\n",(0,i.jsxs)(s.p,{children:["Python provides the ",(0,i.jsx)(s.code,{children:"hashlib"})," library, which offers various hash algorithms. Let's start by hashing a simple string using SHA-256:"]}),"\n",(0,i.jsx)(s.pre,{children:(0,i.jsx)(s.code,{className:"language-python",metastring:'title="Python"',children:'import hashlib\n\ndef compute_sha256_hash(data):\n    sha256_hash = hashlib.sha256(data.encode()).hexdigest()\n    return sha256_hash\n\n# Example usage\nmessage = "Hello, world!"\nhash_value = compute_sha256_hash(message)\nprint(f"SHA-256 hash of \'{message}\':\\n{hash_value}")\n'})}),"\n",(0,i.jsx)(s.pre,{children:(0,i.jsx)(s.code,{children:"SHA-256 hash of 'Hello, world!':\n315f5bdb76d078c43b8ac0064e4a0164612b1fce77c869345bfc94c75894edd3\n"})}),"\n",(0,i.jsxs)(s.p,{children:["In this example, we define a function ",(0,i.jsx)(s.code,{children:"compute_sha256_hash"})," that takes a string input, computes its SHA-256 hash, and returns the hexadecimal representation. Feel free to explore other hash algorithms like MD5 or SHA-1."]}),"\n",(0,i.jsx)(s.h3,{id:"creating-a-simple-hash-table",children:"Creating a Simple Hash Table"}),"\n",(0,i.jsx)(s.p,{children:"Let's create a basic hash table to store key-value pairs. We'll use a dictionary in Python, which internally uses hashing for efficient lookups:"}),"\n",(0,i.jsx)(s.pre,{children:(0,i.jsx)(s.code,{className:"language-python",metastring:'title="Python"',children:"class HashTable:\n    def __init__(self):\n        self.table = {}\n\n    def insert(self, key, value):\n        self.table[key] = value\n\n    def get(self, key):\n        return self.table.get(key, None)\n\n# Example usage\nmy_table = HashTable()\nmy_table.insert(\"apple\", 42)\nmy_table.insert(\"banana\", 17)\n\nprint(f\"Value for 'apple': {my_table.get('apple')}\")\nprint(f\"Value for 'banana': {my_table.get('banana')}\")\n"})}),"\n",(0,i.jsx)(s.pre,{children:(0,i.jsx)(s.code,{children:"Value for 'apple': 42\nValue for 'banana': 17\n"})}),"\n",(0,i.jsx)(s.h3,{id:"file-integrity-checks",children:"File Integrity Checks"}),"\n",(0,i.jsx)(s.p,{children:"Hashing is commonly used to verify file integrity. Suppose you have a file, and you want to ensure it hasn't been tampered with during transfer. Compute the hash (checksum) before sending the file and share it with the recipient. Upon receiving the file, the recipient computes the hash again and compares it with the shared checksum."}),"\n",(0,i.jsx)(s.h2,{id:"tips-and-best-practices",children:"Tips and Best Practices"}),"\n",(0,i.jsx)(s.p,{children:"In this section, we'll explore practical tips and best practices related to hashing in Python. Whether you're a seasoned data scientist or a curious beginner, these insights will enhance your understanding and help you write robust code."}),"\n",(0,i.jsx)(s.h3,{id:"choose-the-right-hash-function",children:"Choose the Right Hash Function"}),"\n",(0,i.jsx)(s.p,{children:"When selecting a hash function, consider your specific use case:"}),"\n",(0,i.jsxs)(s.ul,{children:["\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Security"}),": If you're dealing with sensitive data (passwords, cryptographic keys), opt for a strong hash function like SHA-256 or SHA-512. These functions are designed to resist attacks and collisions."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Speed"}),": For non-cryptographic purposes (e.g., hash tables), choose a faster hash function like MurmurHash or CityHash. These trade security for performance."]}),"\n"]}),"\n",(0,i.jsxs)(s.li,{children:["\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"Uniqueness"}),": If you need unique identifiers (e.g., Git commit hashes), SHA-1 or SHA-256 are excellent choices."]}),"\n"]}),"\n"]}),"\n",(0,i.jsx)(s.h3,{id:"salting-for-passwords",children:"Salting for Passwords"}),"\n",(0,i.jsxs)(s.p,{children:["When hashing passwords, always use ",(0,i.jsx)(s.strong,{children:"salting"}),". Salting involves adding a unique random value (the salt) to the password before hashing. Here's how it works:"]}),"\n",(0,i.jsx)(s.pre,{children:(0,i.jsx)(s.code,{className:"language-python",metastring:'title="Python"',children:'import hashlib\nimport secrets\n\ndef hash_password(password):\n    salt = secrets.token_bytes(16)  # Generate a random salt\n    salted_password = salt + password.encode()\n    hashed_password = hashlib.sha256(salted_password).hexdigest()\n    return hashed_password\n\n# Example usage\nuser_password = "my_secure_password"\nhashed_password = hash_password(user_password)\nprint(f"Hashed password (with salt): {hashed_password}")\n'})}),"\n",(0,i.jsx)(s.pre,{children:(0,i.jsx)(s.code,{children:"Hashed password (with salt):\nd86b76196689d6f3d548daafea207bd24cb71b4e54e75836d6e1ffaf3e84da7c\n"})}),"\n",(0,i.jsx)(s.p,{children:"Salting prevents attackers from using precomputed rainbow tables to crack passwords."}),"\n",(0,i.jsx)(s.h3,{id:"avoid-hash-collisions",children:"Avoid Hash Collisions"}),"\n",(0,i.jsx)(s.p,{children:"While hash collisions (two different inputs producing the same hash) are rare, they can occur. To minimize this risk:"}),"\n",(0,i.jsxs)(s.ul,{children:["\n",(0,i.jsx)(s.li,{children:"Use a strong hash function with a large output size (e.g., SHA-256)."}),"\n",(0,i.jsx)(s.li,{children:"If handling large datasets, consider using a hash-based data structure like a bloom filter to efficiently check for potential collisions."}),"\n"]}),"\n",(0,i.jsx)(s.h2,{id:"faqs",children:"FAQs"}),"\n",(0,i.jsx)(s.p,{children:"Let's address some common questions related to hashing in Python:"}),"\n",(0,i.jsx)(s.p,{children:(0,i.jsx)(s.strong,{children:"Q: Can I use any string as a key for hashing?"})}),"\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"A:"})," Yes, you can hash any string (or even binary data) using hash functions. However, keep in mind that the quality of your hash function matters. A good hash function should distribute keys uniformly to minimize collisions."]}),"\n",(0,i.jsx)(s.p,{children:(0,i.jsx)(s.strong,{children:"Q: How do I handle collisions in hash tables?"})}),"\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"A:"})," Collisions occur when two different keys hash to the same index. To handle collisions:"]}),"\n",(0,i.jsxs)(s.ul,{children:["\n",(0,i.jsxs)(s.li,{children:["Use ",(0,i.jsx)(s.strong,{children:"open addressing"})," (probing) or ",(0,i.jsx)(s.strong,{children:"separate chaining"})," (linked lists) to resolve collisions."]}),"\n",(0,i.jsx)(s.li,{children:"Resize the hash table dynamically to maintain a low load factor."}),"\n"]}),"\n",(0,i.jsx)(s.p,{children:(0,i.jsx)(s.strong,{children:"Q: Is there a perfect hash function that guarantees no collisions?"})}),"\n",(0,i.jsxs)(s.p,{children:[(0,i.jsx)(s.strong,{children:"A:"})," Unfortunately, no. Due to the pigeonhole principle, if you have more keys than possible hash values, collisions are inevitable. However, cryptographic hash functions like SHA-256 provide a very low probability of collisions."]}),"\n",(0,i.jsx)(s.p,{children:"Remember, hashing is a powerful tool, and understanding its nuances ensures effective and secure implementation. Feel free to explore further and experiment with different hash functions in your Python projects! \ud83d\ude80"})]})}function d(e={}){const{wrapper:s}={...(0,a.a)(),...e.components};return s?(0,i.jsx)(s,{...e,children:(0,i.jsx)(c,{...e})}):c(e)}},11151:(e,s,n)=>{n.d(s,{Z:()=>h,a:()=>r});var i=n(67294);const a={},t=i.createContext(a);function r(e){const s=i.useContext(t);return i.useMemo((function(){return"function"==typeof e?e(s):{...s,...e}}),[s,e])}function h(e){let s;return s=e.disableParentContext?"function"==typeof e.components?e.components(a):e.components||a:r(e.components),i.createElement(t.Provider,{value:s},e.children)}}}]);
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-"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[3584],{49878:(e,t,i)=>{i.r(t),i.d(t,{assets:()=>l,contentTitle:()=>o,default:()=>d,frontMatter:()=>s,metadata:()=>r,toc:()=>c});var a=i(85893),n=i(11151);const s={title:"Venn Diagrams: Art & Pitfalls",authors:["mori"],tags:["Data Science","Data Visualization","Venn Diagrams","Pitfalls","Misinterpretations"]},o=void 0,r={permalink:"/portfolio/blog/venn",source:"@site/blog/venn.md",title:"Venn Diagrams: Art & Pitfalls",description:"In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I\u2019ve encountered their allure and their limitations. In this article, we\u2019ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. 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Attempting to represent numerous sets with overlapping circles in a Venn diagram can result in a convoluted and challenging-to-interpret visualization. This limitation highlights the need for data analysts and scientists to consider alternative visualization techniques, such as Euler diagrams, when dealing with larger and more intricate datasets with numerous intersecting subsets."}),"\n",(0,a.jsx)(t.p,{children:(0,a.jsx)(t.img,{alt:"Wikimedia",src:i(64159).Z+"",width:"512",height:"512"})}),"\n",(0,a.jsx)(t.h2,{id:"overlapping-interpretation",children:"Overlapping Interpretation"}),"\n",(0,a.jsx)(t.p,{children:"The interpretation of overlapping regions in Venn diagrams can often be a source of confusion and misinterpretation. While these overlapping areas are intended to represent the elements shared between sets, the extent and significance of this overlap may not be immediately clear. 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Attempting to represent such data in a binary, all-or-nothing fashion within the confines of overlapping circles can lead to a loss of valuable information and potentially mislead viewers. For these scenarios, alternative visualization methods like heatmaps or network graphs may offer a more accurate depiction of the intricate interconnections and degrees of membership present in the dataset."}),"\n",(0,a.jsx)(t.h2,{id:"contextual-misuse",children:"Contextual Misuse"}),"\n",(0,a.jsx)(t.p,{children:"Contextual misuse of Venn diagrams can occur when these visual tools are applied in inappropriate situations or for purposes they were not designed for. While Venn diagrams excel at representing set relationships, they may fall short when used to convey other types of data, such as numerical values or probabilistic information. Employing Venn diagrams in the wrong context can lead to misinterpretations, confusion, or a lack of clarity in conveying the intended message. It\u2019s crucial for data scientists and analysts to exercise discretion and choose the most suitable visualization method based on the nature of the data they are dealing with. By recognizing the limitations of Venn diagrams and reserving them for scenarios where they can provide meaningful insights into set relationships, we can avoid the pitfalls of contextual misuse and ensure that data visualizations effectively communicate the desired information."}),"\n",(0,a.jsx)(t.h2,{id:"lack-of-quantitative-information",children:"Lack of Quantitative Information"}),"\n",(0,a.jsx)(t.p,{children:"A notable limitation of Venn diagrams is their inherent lack of quantitative information. These diagrams are primarily designed to show the relationships and intersections between sets visually, but they do not provide numerical or quantitative data. 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rename to assets/js/e001c432.209b1720.js
index cae3e1b4..ff508a04 100644
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@@ -1 +1 @@
-"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[9126],{40041:(e,i,n)=>{n.r(i),n.d(i,{assets:()=>d,contentTitle:()=>s,default:()=>h,frontMatter:()=>o,metadata:()=>r,toc:()=>l});var a=n(85893),t=n(11151);const o={title:"Anonymization vs. Pseudonymization",authors:["mori"],tags:["Data Privacy","Anonymization","Pseudonymization","Data Science","Python"]},s=void 0,r={permalink:"/portfolio/blog/anonym-pseudonym",source:"@site/blog/anonym-pseudonym.md",title:"Anonymization vs. Pseudonymization",description:"The Yin and Yang of Data Privacy",date:"2024-04-05T14:49:00.000Z",formattedDate:"April 5, 2024",tags:[{label:"Data Privacy",permalink:"/portfolio/blog/tags/data-privacy"},{label:"Anonymization",permalink:"/portfolio/blog/tags/anonymization"},{label:"Pseudonymization",permalink:"/portfolio/blog/tags/pseudonymization"},{label:"Data Science",permalink:"/portfolio/blog/tags/data-science"},{label:"Python",permalink:"/portfolio/blog/tags/python"}],readingTime:7.91,hasTruncateMarker:!0,authors:[{name:"Morteza Hosseini",title:"Data scientist / ML engineer",key:"mori"}],frontMatter:{title:"Anonymization vs. Pseudonymization",authors:["mori"],tags:["Data Privacy","Anonymization","Pseudonymization","Data Science","Python"]},unlisted:!1,nextItem:{title:"Exploring AI Ethics: 15 Key Q&As",permalink:"/portfolio/blog/ethics"}},d={authorsImageUrls:[void 0]},l=[{value:"The Yin and Yang of Data Privacy",id:"the-yin-and-yang-of-data-privacy",level:2},{value:"Unraveling the Mysteries of Anonymization",id:"unraveling-the-mysteries-of-anonymization",level:2},{value:"The Power of Anonymization",id:"the-power-of-anonymization",level:3},{value:"Python: The Data Scientist\u2019s Swiss Army Knife",id:"python-the-data-scientists-swiss-army-knife",level:3},{value:"The Art of Pseudonymization",id:"the-art-of-pseudonymization",level:2},{value:"The Flexibility of Pseudonymization",id:"the-flexibility-of-pseudonymization",level:3},{value:"Python to the Rescue: Implementing Pseudonymization",id:"python-to-the-rescue-implementing-pseudonymization",level:3},{value:"Comparing Anonymization and Pseudonymization",id:"comparing-anonymization-and-pseudonymization",level:2},{value:"Reversibility: A One-Way Street vs. A Two-Way Lane",id:"reversibility-a-one-way-street-vs-a-two-way-lane",level:3},{value:"Data Utility: A Trade-Off",id:"data-utility-a-trade-off",level:3},{value:"Python Implementations: Strengths and Weaknesses",id:"python-implementations-strengths-and-weaknesses",level:3},{value:"Practical Tips and Scenarios",id:"practical-tips-and-scenarios",level:2},{value:"Practical Tips for Responsible Data Handling",id:"practical-tips-for-responsible-data-handling",level:3},{value:"Scenarios for Anonymization and Pseudonymization",id:"scenarios-for-anonymization-and-pseudonymization",level:3}];function c(e){const i={code:"code",em:"em",h2:"h2",h3:"h3",li:"li",ol:"ol",p:"p",pre:"pre",strong:"strong",...(0,t.a)(),...e.components};return(0,a.jsxs)(a.Fragment,{children:[(0,a.jsx)(i.h2,{id:"the-yin-and-yang-of-data-privacy",children:"The Yin and Yang of Data Privacy"}),"\n",(0,a.jsx)(i.p,{children:"In the digital era, data is the new oil. It fuels innovation, drives decision-making, and propels businesses towards success. But with great power comes great responsibility. As data scientists, we must ensure that the data we handle is not misused or exploited, causing harm to the individuals it represents. Enter the twin superheroes of data protection: Anonymization and Pseudonymization."}),"\n",(0,a.jsx)(i.p,{children:"Anonymization and Pseudonymization are two essential techniques in the realm of data privacy. They serve as the yin and yang, balancing the need for data utility with the imperative of privacy protection. While they may seem similar at first glance, each has its distinct characteristics and use cases."}),"\n",(0,a.jsxs)(i.p,{children:[(0,a.jsx)(i.strong,{children:"Anonymization"})," is the process of ",(0,a.jsx)(i.em,{children:"irreversibly"})," transforming data in such a way that a data subject can no longer be identified directly or indirectly. It\u2019s like a one-way ticket: once the data is anonymized, there\u2019s no going back. It\u2019s a powerful tool for protecting privacy, but it also means that certain analyses requiring linkage back to the original data are off the table."]}),"\n",(0,a.jsxs)(i.p,{children:["On the other hand, ",(0,a.jsx)(i.strong,{children:"Pseudonymization"})," is a reversible process that replaces or obscures identifiable data elements within a data record with artificial identifiers or pseudonyms. It\u2019s a bit like a masquerade ball: the data subjects are concealed behind masks, but they can be re-identified if necessary. This makes pseudonymization a flexible tool that can maintain data\u2019s utility while still offering a level of privacy protection."]}),"\n",(0,a.jsx)(i.p,{children:"In this blog post, we\u2019ll dive deep into these two techniques, exploring their nuances, differences, and applications. So, buckle up and get ready for an exciting journey into the world of data privacy!"}),"\n",(0,a.jsx)(i.h2,{id:"unraveling-the-mysteries-of-anonymization",children:"Unraveling the Mysteries of Anonymization"}),"\n",(0,a.jsx)(i.p,{children:"Anonymization is a potent technique that can transform your data into a privacy-compliant asset. In the realm of data privacy, Anonymization refers to the process of removing or altering personally identifiable information from your data so that the individuals whom the data describe remain anonymous."}),"\n",(0,a.jsx)(i.h3,{id:"the-power-of-anonymization",children:"The Power of Anonymization"}),"\n",(0,a.jsx)(i.p,{children:"Imagine you\u2019re a data scientist working with a dataset chock-full of sensitive information. You need to share this data with your team, but you also need to ensure you\u2019re not violating any privacy laws or ethical guidelines. Anonymization comes to your rescue here. By anonymizing your data, you can share it freely without worrying about exposing personal information."}),"\n",(0,a.jsx)(i.h3,{id:"python-the-data-scientists-swiss-army-knife",children:"Python: The Data Scientist\u2019s Swiss Army Knife"}),"\n",(0,a.jsx)(i.p,{children:"Python, with its vast array of libraries and tools, is the perfect companion for a data scientist looking to implement Anonymization. Let\u2019s take a look at a simple example using cryptographic hashing with the hashlib library."}),"\n",(0,a.jsx)(i.pre,{children:(0,a.jsx)(i.code,{className:"language-python",metastring:'title="Python"',children:'import hashlib\nfrom pprint import pprint\n\n# Let\'s consider a simple data list\ndata = ["Alice", "Bob", "Charlie", "Alice", "Bob", "Dave"]\n\n# Anonymize the data using SHA256 hashing\nanonymized_data = [\n    hashlib.sha256(name.encode()).hexdigest() for name in data\n]\n\npprint(anonymized_data)\n'})}),"\n",(0,a.jsx)(i.pre,{children:(0,a.jsx)(i.code,{children:"['3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043',\n'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961',\n'6e81b1255ad51bb201a2b8afa9b66653297ae0217f833b14b39b5231228bf968',\n'3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043',\n'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961',\n'809a721743350c0c49a7b444ad3aeaf1341fdd48d1bf510e08b008edab72dc70']\n"})}),"\n",(0,a.jsx)(i.p,{children:"In this example, we\u2019ve replaced each name in our data with a unique hash generated by the SHA256 algorithm. The original names can\u2019t be retrieved from these hashes, making the data effectively anonymized."}),"\n",(0,a.jsx)(i.p,{children:"However, this is a simple example. Real-world data can be much more complex and may require more sophisticated anonymization techniques. But not to worry! Python\u2019s vast ecosystem of libraries and tools has you covered no matter how complex your data might be."}),"\n",(0,a.jsx)(i.h2,{id:"the-art-of-pseudonymization",children:"The Art of Pseudonymization"}),"\n",(0,a.jsx)(i.p,{children:"After our deep dive into anonymization, let\u2019s now turn our attention to its counterpart, Pseudonymization. In the grand tapestry of data privacy, Pseudonymization is a technique that replaces identifiable data with artificial identifiers or pseudonyms. Unlike anonymization, pseudonymization is reversible under controlled conditions, allowing data subjects to be re-identified when necessary."}),"\n",(0,a.jsx)(i.h3,{id:"the-flexibility-of-pseudonymization",children:"The Flexibility of Pseudonymization"}),"\n",(0,a.jsx)(i.p,{children:"Imagine you\u2019re a healthcare researcher working on a longitudinal study. You need to track individual patients\u2019 health outcomes over time, but you also need to respect their privacy. Pseudonymization is your ally in this scenario. By replacing identifiable information like names or social security numbers with pseudonyms, you can protect your patients\u2019 privacy while still being able to track their data over time."}),"\n",(0,a.jsx)(i.h3,{id:"python-to-the-rescue-implementing-pseudonymization",children:"Python to the Rescue: Implementing Pseudonymization"}),"\n",(0,a.jsxs)(i.p,{children:["Python, with its diverse ecosystem of libraries and tools, makes implementing pseudonymization a breeze. Let\u2019s look at an example using Python\u2019s built-in ",(0,a.jsx)(i.code,{children:"uuid"})," library to generate unique pseudonyms for our data."]}),"\n",(0,a.jsx)(i.pre,{children:(0,a.jsx)(i.code,{className:"language-python",metastring:'title="Python"',children:"import uuid\nfrom pprint import pprint\n\n# Let's consider a simple data list\ndata = ['Alice', 'Bob', 'Charlie', 'Alice', 'Bob', 'Dave']\n\n# Create a dictionary to store our pseudonyms\npseudonyms = {}\n\n# Pseudonymize the data\npseudonymized_data = [\n    pseudonyms.setdefault(name, str(uuid.uuid4())) for name in data\n]\n\npprint(pseudonymized_data)\n"})}),"\n",(0,a.jsx)(i.pre,{children:(0,a.jsx)(i.code,{children:"['dfaa2289-1611-44ea-950a-8c9b43544a68',\n'f20c6a2f-bdc2-40b4-896a-614bec5baa77',\n'9244f9fb-f21b-4111-b982-ce81f9374daf',\n'dfaa2289-1611-44ea-950a-8c9b43544a68',\n'f20c6a2f-bdc2-40b4-896a-614bec5baa77',\n'a2697683-8e88-4b5b-add1-51cc4533ffda']\n"})}),"\n",(0,a.jsxs)(i.p,{children:["In this example, we\u2019ve replaced each name in our data with a unique pseudonym generated by the ",(0,a.jsx)(i.code,{children:"uuid"})," library. The pseudonyms are stored in a dictionary, allowing us to consistently replace each name with the same pseudonym across our data. However, with the dictionary, we can also reverse the pseudonymization process if needed, which is not possible with anonymization."]}),"\n",(0,a.jsx)(i.pre,{children:(0,a.jsx)(i.code,{className:"language-python",metastring:'title="Python"',children:"pprint(pseudonyms)\n"})}),"\n",(0,a.jsx)(i.pre,{children:(0,a.jsx)(i.code,{children:"{'Alice': 'dfaa2289-1611-44ea-950a-8c9b43544a68',\n'Bob': 'f20c6a2f-bdc2-40b4-896a-614bec5baa77',\n'Charlie': '9244f9fb-f21b-4111-b982-ce81f9374daf',\n'Dave': 'a2697683-8e88-4b5b-add1-51cc4533ffda'}\n"})}),"\n",(0,a.jsx)(i.h2,{id:"comparing-anonymization-and-pseudonymization",children:"Comparing Anonymization and Pseudonymization"}),"\n",(0,a.jsx)(i.p,{children:"Now that we\u2019ve explored both anonymization and pseudonymization, let\u2019s take a step back and compare these two techniques. While they both aim to protect privacy, their differences in reversibility, data utility, and risk levels make them suitable for different scenarios."}),"\n",(0,a.jsx)(i.h3,{id:"reversibility-a-one-way-street-vs-a-two-way-lane",children:"Reversibility: A One-Way Street vs. A Two-Way Lane"}),"\n",(0,a.jsx)(i.p,{children:"Anonymization is a one-way street. Once data is anonymized, it\u2019s impossible to revert it back to its original form. This makes anonymization a strong tool for privacy protection, as it eliminates the risk of re-identification."}),"\n",(0,a.jsx)(i.p,{children:"On the contrary, pseudonymization is a two-way lane. It replaces identifiable data with pseudonyms, but this process can be reversed under controlled conditions. This allows for the possibility of re-identification, which can be beneficial in certain scenarios, such as longitudinal studies or customer relationship management."}),"\n",(0,a.jsx)(i.h3,{id:"data-utility-a-trade-off",children:"Data Utility: A Trade-Off"}),"\n",(0,a.jsx)(i.p,{children:"Anonymization provides a high level of privacy protection, but it comes at the cost of data utility. By irreversibly transforming data, anonymization can limit the types of analysis that can be performed. For example, it\u2019s impossible to conduct individual-level analysis or link data across multiple datasets once it\u2019s been anonymized."}),"\n",(0,a.jsx)(i.p,{children:"Pseudonymization, on the other hand, maintains a higher level of data utility. By allowing re-identification, pseudonymization enables individual-level analysis and data linkage. However, this comes with a higher risk of privacy breaches compared to anonymization."}),"\n",(0,a.jsx)(i.h3,{id:"python-implementations-strengths-and-weaknesses",children:"Python Implementations: Strengths and Weaknesses"}),"\n",(0,a.jsx)(i.p,{children:"When it comes to implementing these techniques in Python, both have their strengths and weaknesses."}),"\n",(0,a.jsx)(i.p,{children:"Anonymization, as demonstrated with the cryptographic hashing example, provides strong privacy guarantees and is straightforward to implement. However, its irreversible nature means that it loses the ability to link back to the original data, limiting its utility in certain scenarios."}),"\n",(0,a.jsxs)(i.p,{children:["Pseudonymization, as shown through the use of Python\u2019s ",(0,a.jsx)(i.code,{children:"uuid"})," library, is also easy to implement and allows for re-identification, maintaining a higher level of data utility. However, it can be vulnerable to linkage attacks if not properly managed, and the responsibility to protect the pseudonym-to-identity mapping falls on the data handler."]}),"\n",(0,a.jsx)(i.h2,{id:"practical-tips-and-scenarios",children:"Practical Tips and Scenarios"}),"\n",(0,a.jsx)(i.p,{children:"As we\u2019ve seen, both anonymization and pseudonymization have their strengths and weaknesses. Choosing the right technique depends on the specific requirements of your data project. To help you make the right decision, let\u2019s discuss some practical tips and scenarios."}),"\n",(0,a.jsx)(i.h3,{id:"practical-tips-for-responsible-data-handling",children:"Practical Tips for Responsible Data Handling"}),"\n",(0,a.jsxs)(i.ol,{children:["\n",(0,a.jsxs)(i.li,{children:[(0,a.jsx)(i.strong,{children:"Understand Your Data:"})," Before you decide on a data protection technique, it\u2019s crucial to understand your data. What kind of data are you dealing with? What level of privacy protection does it need? Answering these questions will help you choose the right technique."]}),"\n",(0,a.jsxs)(i.li,{children:[(0,a.jsx)(i.strong,{children:"Know Your Legal Obligations:"})," Depending on your jurisdiction and the nature of your data, you may be subject to specific data protection laws. Make sure you\u2019re aware of these laws and that your data handling practices comply with them."]}),"\n",(0,a.jsxs)(i.li,{children:[(0,a.jsx)(i.strong,{children:"Secure Your Pseudonymization Keys:"})," If you\u2019re using pseudonymization, remember that your pseudonyms can be reversed to reveal the original data. Therefore, it\u2019s crucial to secure your pseudonymization keys. If these keys fall into the wrong hands, your data\u2019s privacy could be compromised."]}),"\n",(0,a.jsxs)(i.li,{children:[(0,a.jsx)(i.strong,{children:"Consider the Trade-off:"})," Remember that there\u2019s a trade-off between privacy protection and data utility. Anonymization provides stronger privacy protection but limits data utility, while pseudonymization maintains data utility but comes with a higher risk of privacy breaches."]}),"\n"]}),"\n",(0,a.jsx)(i.h3,{id:"scenarios-for-anonymization-and-pseudonymization",children:"Scenarios for Anonymization and Pseudonymization"}),"\n",(0,a.jsxs)(i.ol,{children:["\n",(0,a.jsxs)(i.li,{children:[(0,a.jsx)(i.strong,{children:"Public Data Release:"})," If you\u2019re releasing a dataset to the public, anonymization is your best bet. This will ensure that individuals in the dataset cannot be identified, protecting their privacy."]}),"\n",(0,a.jsxs)(i.li,{children:[(0,a.jsx)(i.strong,{children:"Longitudinal Studies:"})," For longitudinal studies that require tracking individual subjects over time, pseudonymization is a good choice. It allows you to protect your subjects\u2019 privacy while maintaining the ability to link their data across time points."]}),"\n",(0,a.jsxs)(i.li,{children:[(0,a.jsx)(i.strong,{children:"Customer Relationship Management:"})," In scenarios where you need to maintain a relationship with your data subjects, such as in customer relationship management, pseudonymization can be beneficial. It allows you to protect your customers\u2019 privacy while still being able to identify them when necessary."]}),"\n"]}),"\n",(0,a.jsx)(i.p,{children:"By now, you should have a deep understanding of both anonymization and pseudonymization, and you should be equipped with practical knowledge to implement these techniques in your own data science endeavors. Remember, data privacy is a journey, not a destination. As data handlers, it\u2019s our responsibility to continuously optimize our data protection techniques using the powerful tools that Python provides."})]})}function h(e={}){const{wrapper:i}={...(0,t.a)(),...e.components};return i?(0,a.jsx)(i,{...e,children:(0,a.jsx)(c,{...e})}):c(e)}},11151:(e,i,n)=>{n.d(i,{Z:()=>r,a:()=>s});var a=n(67294);const t={},o=a.createContext(t);function s(e){const i=a.useContext(o);return a.useMemo((function(){return"function"==typeof e?e(i):{...i,...e}}),[i,e])}function r(e){let i;return i=e.disableParentContext?"function"==typeof e.components?e.components(t):e.components||t:s(e.components),a.createElement(o.Provider,{value:i},e.children)}}}]);
\ No newline at end of file
+"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[9126],{40041:(e,i,n)=>{n.r(i),n.d(i,{assets:()=>d,contentTitle:()=>s,default:()=>h,frontMatter:()=>o,metadata:()=>r,toc:()=>l});var a=n(85893),t=n(11151);const o={title:"Anonymization vs. Pseudonymization",authors:["mori"],tags:["Data Privacy","Anonymization","Pseudonymization","Data Science","Python"]},s=void 0,r={permalink:"/portfolio/blog/anonym-pseudonym",source:"@site/blog/anonym-pseudonym.md",title:"Anonymization vs. Pseudonymization",description:"The Yin and Yang of Data Privacy",date:"2024-04-09T10:32:08.000Z",formattedDate:"April 9, 2024",tags:[{label:"Data Privacy",permalink:"/portfolio/blog/tags/data-privacy"},{label:"Anonymization",permalink:"/portfolio/blog/tags/anonymization"},{label:"Pseudonymization",permalink:"/portfolio/blog/tags/pseudonymization"},{label:"Data Science",permalink:"/portfolio/blog/tags/data-science"},{label:"Python",permalink:"/portfolio/blog/tags/python"}],readingTime:7.91,hasTruncateMarker:!0,authors:[{name:"Morteza Hosseini",title:"Data scientist / ML engineer",key:"mori"}],frontMatter:{title:"Anonymization vs. Pseudonymization",authors:["mori"],tags:["Data Privacy","Anonymization","Pseudonymization","Data Science","Python"]},unlisted:!1,nextItem:{title:"Exploring AI Ethics: 15 Key Q&As",permalink:"/portfolio/blog/ethics"}},d={authorsImageUrls:[void 0]},l=[{value:"The Yin and Yang of Data Privacy",id:"the-yin-and-yang-of-data-privacy",level:2},{value:"Unraveling the Mysteries of Anonymization",id:"unraveling-the-mysteries-of-anonymization",level:2},{value:"The Power of Anonymization",id:"the-power-of-anonymization",level:3},{value:"Python: The Data Scientist\u2019s Swiss Army Knife",id:"python-the-data-scientists-swiss-army-knife",level:3},{value:"The Art of Pseudonymization",id:"the-art-of-pseudonymization",level:2},{value:"The Flexibility of Pseudonymization",id:"the-flexibility-of-pseudonymization",level:3},{value:"Python to the Rescue: Implementing Pseudonymization",id:"python-to-the-rescue-implementing-pseudonymization",level:3},{value:"Comparing Anonymization and Pseudonymization",id:"comparing-anonymization-and-pseudonymization",level:2},{value:"Reversibility: A One-Way Street vs. A Two-Way Lane",id:"reversibility-a-one-way-street-vs-a-two-way-lane",level:3},{value:"Data Utility: A Trade-Off",id:"data-utility-a-trade-off",level:3},{value:"Python Implementations: Strengths and Weaknesses",id:"python-implementations-strengths-and-weaknesses",level:3},{value:"Practical Tips and Scenarios",id:"practical-tips-and-scenarios",level:2},{value:"Practical Tips for Responsible Data Handling",id:"practical-tips-for-responsible-data-handling",level:3},{value:"Scenarios for Anonymization and Pseudonymization",id:"scenarios-for-anonymization-and-pseudonymization",level:3}];function c(e){const i={code:"code",em:"em",h2:"h2",h3:"h3",li:"li",ol:"ol",p:"p",pre:"pre",strong:"strong",...(0,t.a)(),...e.components};return(0,a.jsxs)(a.Fragment,{children:[(0,a.jsx)(i.h2,{id:"the-yin-and-yang-of-data-privacy",children:"The Yin and Yang of Data Privacy"}),"\n",(0,a.jsx)(i.p,{children:"In the digital era, data is the new oil. It fuels innovation, drives decision-making, and propels businesses towards success. But with great power comes great responsibility. As data scientists, we must ensure that the data we handle is not misused or exploited, causing harm to the individuals it represents. Enter the twin superheroes of data protection: Anonymization and Pseudonymization."}),"\n",(0,a.jsx)(i.p,{children:"Anonymization and Pseudonymization are two essential techniques in the realm of data privacy. They serve as the yin and yang, balancing the need for data utility with the imperative of privacy protection. While they may seem similar at first glance, each has its distinct characteristics and use cases."}),"\n",(0,a.jsxs)(i.p,{children:[(0,a.jsx)(i.strong,{children:"Anonymization"})," is the process of ",(0,a.jsx)(i.em,{children:"irreversibly"})," transforming data in such a way that a data subject can no longer be identified directly or indirectly. It\u2019s like a one-way ticket: once the data is anonymized, there\u2019s no going back. It\u2019s a powerful tool for protecting privacy, but it also means that certain analyses requiring linkage back to the original data are off the table."]}),"\n",(0,a.jsxs)(i.p,{children:["On the other hand, ",(0,a.jsx)(i.strong,{children:"Pseudonymization"})," is a reversible process that replaces or obscures identifiable data elements within a data record with artificial identifiers or pseudonyms. It\u2019s a bit like a masquerade ball: the data subjects are concealed behind masks, but they can be re-identified if necessary. This makes pseudonymization a flexible tool that can maintain data\u2019s utility while still offering a level of privacy protection."]}),"\n",(0,a.jsx)(i.p,{children:"In this blog post, we\u2019ll dive deep into these two techniques, exploring their nuances, differences, and applications. So, buckle up and get ready for an exciting journey into the world of data privacy!"}),"\n",(0,a.jsx)(i.h2,{id:"unraveling-the-mysteries-of-anonymization",children:"Unraveling the Mysteries of Anonymization"}),"\n",(0,a.jsx)(i.p,{children:"Anonymization is a potent technique that can transform your data into a privacy-compliant asset. In the realm of data privacy, Anonymization refers to the process of removing or altering personally identifiable information from your data so that the individuals whom the data describe remain anonymous."}),"\n",(0,a.jsx)(i.h3,{id:"the-power-of-anonymization",children:"The Power of Anonymization"}),"\n",(0,a.jsx)(i.p,{children:"Imagine you\u2019re a data scientist working with a dataset chock-full of sensitive information. You need to share this data with your team, but you also need to ensure you\u2019re not violating any privacy laws or ethical guidelines. Anonymization comes to your rescue here. By anonymizing your data, you can share it freely without worrying about exposing personal information."}),"\n",(0,a.jsx)(i.h3,{id:"python-the-data-scientists-swiss-army-knife",children:"Python: The Data Scientist\u2019s Swiss Army Knife"}),"\n",(0,a.jsx)(i.p,{children:"Python, with its vast array of libraries and tools, is the perfect companion for a data scientist looking to implement Anonymization. Let\u2019s take a look at a simple example using cryptographic hashing with the hashlib library."}),"\n",(0,a.jsx)(i.pre,{children:(0,a.jsx)(i.code,{className:"language-python",metastring:'title="Python"',children:'import hashlib\nfrom pprint import pprint\n\n# Let\'s consider a simple data list\ndata = ["Alice", "Bob", "Charlie", "Alice", "Bob", "Dave"]\n\n# Anonymize the data using SHA256 hashing\nanonymized_data = [\n    hashlib.sha256(name.encode()).hexdigest() for name in data\n]\n\npprint(anonymized_data)\n'})}),"\n",(0,a.jsx)(i.pre,{children:(0,a.jsx)(i.code,{children:"['3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043',\n'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961',\n'6e81b1255ad51bb201a2b8afa9b66653297ae0217f833b14b39b5231228bf968',\n'3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043',\n'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961',\n'809a721743350c0c49a7b444ad3aeaf1341fdd48d1bf510e08b008edab72dc70']\n"})}),"\n",(0,a.jsx)(i.p,{children:"In this example, we\u2019ve replaced each name in our data with a unique hash generated by the SHA256 algorithm. The original names can\u2019t be retrieved from these hashes, making the data effectively anonymized."}),"\n",(0,a.jsx)(i.p,{children:"However, this is a simple example. Real-world data can be much more complex and may require more sophisticated anonymization techniques. But not to worry! Python\u2019s vast ecosystem of libraries and tools has you covered no matter how complex your data might be."}),"\n",(0,a.jsx)(i.h2,{id:"the-art-of-pseudonymization",children:"The Art of Pseudonymization"}),"\n",(0,a.jsx)(i.p,{children:"After our deep dive into anonymization, let\u2019s now turn our attention to its counterpart, Pseudonymization. In the grand tapestry of data privacy, Pseudonymization is a technique that replaces identifiable data with artificial identifiers or pseudonyms. Unlike anonymization, pseudonymization is reversible under controlled conditions, allowing data subjects to be re-identified when necessary."}),"\n",(0,a.jsx)(i.h3,{id:"the-flexibility-of-pseudonymization",children:"The Flexibility of Pseudonymization"}),"\n",(0,a.jsx)(i.p,{children:"Imagine you\u2019re a healthcare researcher working on a longitudinal study. You need to track individual patients\u2019 health outcomes over time, but you also need to respect their privacy. Pseudonymization is your ally in this scenario. By replacing identifiable information like names or social security numbers with pseudonyms, you can protect your patients\u2019 privacy while still being able to track their data over time."}),"\n",(0,a.jsx)(i.h3,{id:"python-to-the-rescue-implementing-pseudonymization",children:"Python to the Rescue: Implementing Pseudonymization"}),"\n",(0,a.jsxs)(i.p,{children:["Python, with its diverse ecosystem of libraries and tools, makes implementing pseudonymization a breeze. Let\u2019s look at an example using Python\u2019s built-in ",(0,a.jsx)(i.code,{children:"uuid"})," library to generate unique pseudonyms for our data."]}),"\n",(0,a.jsx)(i.pre,{children:(0,a.jsx)(i.code,{className:"language-python",metastring:'title="Python"',children:"import uuid\nfrom pprint import pprint\n\n# Let's consider a simple data list\ndata = ['Alice', 'Bob', 'Charlie', 'Alice', 'Bob', 'Dave']\n\n# Create a dictionary to store our pseudonyms\npseudonyms = {}\n\n# Pseudonymize the data\npseudonymized_data = [\n    pseudonyms.setdefault(name, str(uuid.uuid4())) for name in data\n]\n\npprint(pseudonymized_data)\n"})}),"\n",(0,a.jsx)(i.pre,{children:(0,a.jsx)(i.code,{children:"['dfaa2289-1611-44ea-950a-8c9b43544a68',\n'f20c6a2f-bdc2-40b4-896a-614bec5baa77',\n'9244f9fb-f21b-4111-b982-ce81f9374daf',\n'dfaa2289-1611-44ea-950a-8c9b43544a68',\n'f20c6a2f-bdc2-40b4-896a-614bec5baa77',\n'a2697683-8e88-4b5b-add1-51cc4533ffda']\n"})}),"\n",(0,a.jsxs)(i.p,{children:["In this example, we\u2019ve replaced each name in our data with a unique pseudonym generated by the ",(0,a.jsx)(i.code,{children:"uuid"})," library. The pseudonyms are stored in a dictionary, allowing us to consistently replace each name with the same pseudonym across our data. However, with the dictionary, we can also reverse the pseudonymization process if needed, which is not possible with anonymization."]}),"\n",(0,a.jsx)(i.pre,{children:(0,a.jsx)(i.code,{className:"language-python",metastring:'title="Python"',children:"pprint(pseudonyms)\n"})}),"\n",(0,a.jsx)(i.pre,{children:(0,a.jsx)(i.code,{children:"{'Alice': 'dfaa2289-1611-44ea-950a-8c9b43544a68',\n'Bob': 'f20c6a2f-bdc2-40b4-896a-614bec5baa77',\n'Charlie': '9244f9fb-f21b-4111-b982-ce81f9374daf',\n'Dave': 'a2697683-8e88-4b5b-add1-51cc4533ffda'}\n"})}),"\n",(0,a.jsx)(i.h2,{id:"comparing-anonymization-and-pseudonymization",children:"Comparing Anonymization and Pseudonymization"}),"\n",(0,a.jsx)(i.p,{children:"Now that we\u2019ve explored both anonymization and pseudonymization, let\u2019s take a step back and compare these two techniques. While they both aim to protect privacy, their differences in reversibility, data utility, and risk levels make them suitable for different scenarios."}),"\n",(0,a.jsx)(i.h3,{id:"reversibility-a-one-way-street-vs-a-two-way-lane",children:"Reversibility: A One-Way Street vs. A Two-Way Lane"}),"\n",(0,a.jsx)(i.p,{children:"Anonymization is a one-way street. Once data is anonymized, it\u2019s impossible to revert it back to its original form. This makes anonymization a strong tool for privacy protection, as it eliminates the risk of re-identification."}),"\n",(0,a.jsx)(i.p,{children:"On the contrary, pseudonymization is a two-way lane. It replaces identifiable data with pseudonyms, but this process can be reversed under controlled conditions. This allows for the possibility of re-identification, which can be beneficial in certain scenarios, such as longitudinal studies or customer relationship management."}),"\n",(0,a.jsx)(i.h3,{id:"data-utility-a-trade-off",children:"Data Utility: A Trade-Off"}),"\n",(0,a.jsx)(i.p,{children:"Anonymization provides a high level of privacy protection, but it comes at the cost of data utility. By irreversibly transforming data, anonymization can limit the types of analysis that can be performed. For example, it\u2019s impossible to conduct individual-level analysis or link data across multiple datasets once it\u2019s been anonymized."}),"\n",(0,a.jsx)(i.p,{children:"Pseudonymization, on the other hand, maintains a higher level of data utility. By allowing re-identification, pseudonymization enables individual-level analysis and data linkage. However, this comes with a higher risk of privacy breaches compared to anonymization."}),"\n",(0,a.jsx)(i.h3,{id:"python-implementations-strengths-and-weaknesses",children:"Python Implementations: Strengths and Weaknesses"}),"\n",(0,a.jsx)(i.p,{children:"When it comes to implementing these techniques in Python, both have their strengths and weaknesses."}),"\n",(0,a.jsx)(i.p,{children:"Anonymization, as demonstrated with the cryptographic hashing example, provides strong privacy guarantees and is straightforward to implement. However, its irreversible nature means that it loses the ability to link back to the original data, limiting its utility in certain scenarios."}),"\n",(0,a.jsxs)(i.p,{children:["Pseudonymization, as shown through the use of Python\u2019s ",(0,a.jsx)(i.code,{children:"uuid"})," library, is also easy to implement and allows for re-identification, maintaining a higher level of data utility. However, it can be vulnerable to linkage attacks if not properly managed, and the responsibility to protect the pseudonym-to-identity mapping falls on the data handler."]}),"\n",(0,a.jsx)(i.h2,{id:"practical-tips-and-scenarios",children:"Practical Tips and Scenarios"}),"\n",(0,a.jsx)(i.p,{children:"As we\u2019ve seen, both anonymization and pseudonymization have their strengths and weaknesses. Choosing the right technique depends on the specific requirements of your data project. To help you make the right decision, let\u2019s discuss some practical tips and scenarios."}),"\n",(0,a.jsx)(i.h3,{id:"practical-tips-for-responsible-data-handling",children:"Practical Tips for Responsible Data Handling"}),"\n",(0,a.jsxs)(i.ol,{children:["\n",(0,a.jsxs)(i.li,{children:[(0,a.jsx)(i.strong,{children:"Understand Your Data:"})," Before you decide on a data protection technique, it\u2019s crucial to understand your data. What kind of data are you dealing with? What level of privacy protection does it need? Answering these questions will help you choose the right technique."]}),"\n",(0,a.jsxs)(i.li,{children:[(0,a.jsx)(i.strong,{children:"Know Your Legal Obligations:"})," Depending on your jurisdiction and the nature of your data, you may be subject to specific data protection laws. Make sure you\u2019re aware of these laws and that your data handling practices comply with them."]}),"\n",(0,a.jsxs)(i.li,{children:[(0,a.jsx)(i.strong,{children:"Secure Your Pseudonymization Keys:"})," If you\u2019re using pseudonymization, remember that your pseudonyms can be reversed to reveal the original data. Therefore, it\u2019s crucial to secure your pseudonymization keys. If these keys fall into the wrong hands, your data\u2019s privacy could be compromised."]}),"\n",(0,a.jsxs)(i.li,{children:[(0,a.jsx)(i.strong,{children:"Consider the Trade-off:"})," Remember that there\u2019s a trade-off between privacy protection and data utility. Anonymization provides stronger privacy protection but limits data utility, while pseudonymization maintains data utility but comes with a higher risk of privacy breaches."]}),"\n"]}),"\n",(0,a.jsx)(i.h3,{id:"scenarios-for-anonymization-and-pseudonymization",children:"Scenarios for Anonymization and Pseudonymization"}),"\n",(0,a.jsxs)(i.ol,{children:["\n",(0,a.jsxs)(i.li,{children:[(0,a.jsx)(i.strong,{children:"Public Data Release:"})," If you\u2019re releasing a dataset to the public, anonymization is your best bet. This will ensure that individuals in the dataset cannot be identified, protecting their privacy."]}),"\n",(0,a.jsxs)(i.li,{children:[(0,a.jsx)(i.strong,{children:"Longitudinal Studies:"})," For longitudinal studies that require tracking individual subjects over time, pseudonymization is a good choice. It allows you to protect your subjects\u2019 privacy while maintaining the ability to link their data across time points."]}),"\n",(0,a.jsxs)(i.li,{children:[(0,a.jsx)(i.strong,{children:"Customer Relationship Management:"})," In scenarios where you need to maintain a relationship with your data subjects, such as in customer relationship management, pseudonymization can be beneficial. It allows you to protect your customers\u2019 privacy while still being able to identify them when necessary."]}),"\n"]}),"\n",(0,a.jsx)(i.p,{children:"By now, you should have a deep understanding of both anonymization and pseudonymization, and you should be equipped with practical knowledge to implement these techniques in your own data science endeavors. Remember, data privacy is a journey, not a destination. As data handlers, it\u2019s our responsibility to continuously optimize our data protection techniques using the powerful tools that Python provides."})]})}function h(e={}){const{wrapper:i}={...(0,t.a)(),...e.components};return i?(0,a.jsx)(i,{...e,children:(0,a.jsx)(c,{...e})}):c(e)}},11151:(e,i,n)=>{n.d(i,{Z:()=>r,a:()=>s});var a=n(67294);const t={},o=a.createContext(t);function s(e){const i=a.useContext(o);return a.useMemo((function(){return"function"==typeof e?e(i):{...i,...e}}),[i,e])}function r(e){let i;return i=e.disableParentContext?"function"==typeof e.components?e.components(t):e.components||t:s(e.components),a.createElement(o.Provider,{value:i},e.children)}}}]);
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-"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[7439],{31426:(e,a,t)=>{t.r(a),t.d(a,{assets:()=>d,contentTitle:()=>o,default:()=>h,frontMatter:()=>n,metadata:()=>i,toc:()=>c});var r=t(85893),s=t(11151);const n={title:"Why is Parquet format so popular?",authors:["mori"],tags:["Data Science","Parquet","Data Storage","Spark","Pandas Dataframe"]},o=void 0,i={permalink:"/portfolio/blog/parquet",source:"@site/blog/parquet.md",title:"Why is Parquet format so popular?",description:"Introduction",date:"2024-04-05T14:49:00.000Z",formattedDate:"April 5, 2024",tags:[{label:"Data Science",permalink:"/portfolio/blog/tags/data-science"},{label:"Parquet",permalink:"/portfolio/blog/tags/parquet"},{label:"Data Storage",permalink:"/portfolio/blog/tags/data-storage"},{label:"Spark",permalink:"/portfolio/blog/tags/spark"},{label:"Pandas Dataframe",permalink:"/portfolio/blog/tags/pandas-dataframe"}],readingTime:3.92,hasTruncateMarker:!0,authors:[{name:"Morteza Hosseini",title:"Data scientist / ML engineer",key:"mori"}],frontMatter:{title:"Why is Parquet format so popular?",authors:["mori"],tags:["Data Science","Parquet","Data Storage","Spark","Pandas Dataframe"]},unlisted:!1,prevItem:{title:"Introducing Keras Core",permalink:"/portfolio/blog/keras-core"},nextItem:{title:"Venn Diagrams: Art & Pitfalls",permalink:"/portfolio/blog/venn"}},d={authorsImageUrls:[void 0]},c=[{value:"Introduction",id:"introduction",level:2},{value:"History",id:"history",level:2},{value:"Why so popular?",id:"why-so-popular",level:2},{value:"Parquet vs pandas DataFrame",id:"parquet-vs-pandas-dataframe",level:2},{value:"Conclusion",id:"conclusion",level:2}];function l(e){const a={a:"a",h2:"h2",img:"img",p:"p",table:"table",tbody:"tbody",td:"td",th:"th",thead:"thead",tr:"tr",...(0,s.a)(),...e.components};return(0,r.jsxs)(r.Fragment,{children:[(0,r.jsx)(a.h2,{id:"introduction",children:"Introduction"}),"\n",(0,r.jsxs)(a.p,{children:[(0,r.jsx)(a.a,{href:"https://parquet.apache.org/",children:"Parquet"})," is a popular columnar storage format for big data processing. It\u2019s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we\u2019ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we\u2019ll compare it to the popular pandas DataFrame."]}),"\n",(0,r.jsx)(a.h2,{id:"history",children:"History"}),"\n",(0,r.jsx)(a.p,{children:"The Parquet format was created in 2013 by the Apache Software Foundation\u2019s Parquet project, a collaboration between Twitter, Cloudera, and other organizations. The goal of the project was to create a columnar storage format that was optimized for big data processing and could be used with a variety of data processing frameworks such as Hadoop, Impala, and Hive. The project was a response to the growing need for a more efficient way of storing and processing large datasets as data collection and storage was rapidly increasing. Since its release, the Parquet format has become one of the most popular storage formats for big data, widely used in the industry and adopted by many companies."}),"\n",(0,r.jsx)(a.h2,{id:"why-so-popular",children:"Why so popular?"}),"\n",(0,r.jsx)(a.p,{children:"The first reason why Parquet is so popular is its high compression and encoding capabilities. Parquet uses a technique called columnar storage, which organizes data in a way that allows for more efficient compression and encoding. This means that data stored in Parquet format takes up less space on disk and can be read and processed more quickly."}),"\n",(0,r.jsx)(a.p,{children:"Another benefit of Parquet is its support for advanced data types and encoding schemes. Parquet supports a wide range of data types, including integers, floating-point numbers, strings, and timestamps. It also supports advanced encoding schemes like dictionary encoding, which can further reduce the size of the data on disk."}),"\n",(0,r.jsx)(a.p,{children:"Parquet also supports advanced data querying capabilities. It provides a feature called predicate pushdown, which allows query engines to filter data on disk before reading it into memory. This reduces the amount of data that needs to be read and processed, and can greatly improve query performance."}),"\n",(0,r.jsx)(a.p,{children:"In addition to its high compression and encoding capabilities, Parquet is also highly interoperable. It\u2019s supported by a wide range of big data processing tools and platforms, including Hadoop, Spark, Hive, and Pig. This means that data stored in Parquet format can be easily read and processed by a wide range of tools and platforms, making it a versatile format for big data processing tasks."}),"\n",(0,r.jsx)(a.p,{children:"Finally, Parquet is an open-source format, which means that it can be freely used and modified by anyone. This has led to a large and active community of developers working on the format, which has helped to improve its performance and capabilities over time."}),"\n",(0,r.jsx)(a.h2,{id:"parquet-vs-pandas-dataframe",children:"Parquet vs pandas DataFrame"}),"\n",(0,r.jsxs)(a.table,{children:[(0,r.jsx)(a.thead,{children:(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.th,{children:"Feature"}),(0,r.jsx)(a.th,{children:"Parquet"}),(0,r.jsx)(a.th,{children:"pandas DataFrames"})]})}),(0,r.jsxs)(a.tbody,{children:[(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.td,{children:"Storage format"}),(0,r.jsx)(a.td,{children:"Columnar"}),(0,r.jsx)(a.td,{children:"Row-based"})]}),(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.td,{children:"Compression"}),(0,r.jsx)(a.td,{children:"High"}),(0,r.jsx)(a.td,{children:"Low"})]}),(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.td,{children:"Encoding"}),(0,r.jsx)(a.td,{children:"Advanced (dictionary, RLE, etc.)"}),(0,r.jsx)(a.td,{children:"Basic"})]}),(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.td,{children:"Data querying"}),(0,r.jsx)(a.td,{children:"Advanced (predicate pushdown)"}),(0,r.jsx)(a.td,{children:"Basic"})]}),(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.td,{children:"Handling large data"}),(0,r.jsx)(a.td,{children:"Efficiently handle large data"}),(0,r.jsx)(a.td,{children:"Not optimized for large data processing"})]}),(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.td,{children:"Execution"}),(0,r.jsx)(a.td,{children:"Can be executed in distributed systems"}),(0,r.jsx)(a.td,{children:"Executed on single machine"})]}),(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.td,{children:"Open-source"}),(0,r.jsx)(a.td,{children:"Yes"}),(0,r.jsx)(a.td,{children:"Yes"})]})]})]}),"\n",(0,r.jsxs)(a.p,{children:["As you can see, Parquet and ",(0,r.jsx)(a.a,{href:"https://pandas.pydata.org/",children:"pandas"})," DataFrames have some similarities, but they also have some important differences. Parquet is a columnar storage format that is optimized for big data processing and provides advanced compression and encoding capabilities. On the other hand, pandas DataFrames are row-based and are more commonly used for data analysis and manipulation."]}),"\n",(0,r.jsx)(a.p,{children:"One of the main benefits of using Parquet is that it can greatly improve the performance and efficiency of big data processing tasks by reducing the amount of data that needs to be read and processed. pandas DataFrames, on the other hand, are not as optimized for big data processing and may not be as efficient when working with large datasets."}),"\n",(0,r.jsxs)(a.p,{children:[(0,r.jsx)(a.img,{src:t(9979).Z+"",width:"1400",height:"1372"}),"\n",(0,r.jsx)(a.a,{href:"https://timepasstechies.com/row-oriented-column-oriented-file-formats-hadoop/",children:"https://timepasstechies.com/row-oriented-column-oriented-file-formats-hadoop/"})]}),"\n",(0,r.jsx)(a.p,{children:"Another benefit of Parquet is that it supports a wide range of data types and encoding schemes, which can further reduce the size of the data on disk. pandas DataFrames, on the other hand, have a limited set of data types and do not support advanced encoding schemes."}),"\n",(0,r.jsx)(a.h2,{id:"conclusion",children:"Conclusion"}),"\n",(0,r.jsx)(a.p,{children:"Parquet is a popular columnar storage format for big data processing due to its high compression and encoding capabilities, support for advanced data types and encoding schemes, advanced data querying capabilities, high interoperability, and the fact that it is open-source. These benefits make it a powerful and versatile format for big data processing tasks, and it\u2019s likely to continue to be widely used in the future."})]})}function h(e={}){const{wrapper:a}={...(0,s.a)(),...e.components};return a?(0,r.jsx)(a,{...e,children:(0,r.jsx)(l,{...e})}):l(e)}},9979:(e,a,t)=>{t.d(a,{Z:()=>r});const r=t.p+"assets/images/parquet-row-col-dc910928bf3092a41c8857c877007e38.jpeg"},11151:(e,a,t)=>{t.d(a,{Z:()=>i,a:()=>o});var r=t(67294);const s={},n=r.createContext(s);function o(e){const a=r.useContext(n);return r.useMemo((function(){return"function"==typeof e?e(a):{...a,...e}}),[a,e])}function i(e){let a;return a=e.disableParentContext?"function"==typeof e.components?e.components(s):e.components||s:o(e.components),r.createElement(n.Provider,{value:a},e.children)}}}]);
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+"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[7439],{31426:(e,a,t)=>{t.r(a),t.d(a,{assets:()=>d,contentTitle:()=>o,default:()=>h,frontMatter:()=>n,metadata:()=>i,toc:()=>c});var r=t(85893),s=t(11151);const n={title:"Why is Parquet format so popular?",authors:["mori"],tags:["Data Science","Parquet","Data Storage","Spark","Pandas Dataframe"]},o=void 0,i={permalink:"/portfolio/blog/parquet",source:"@site/blog/parquet.md",title:"Why is Parquet format so popular?",description:"Introduction",date:"2024-04-09T10:32:08.000Z",formattedDate:"April 9, 2024",tags:[{label:"Data Science",permalink:"/portfolio/blog/tags/data-science"},{label:"Parquet",permalink:"/portfolio/blog/tags/parquet"},{label:"Data Storage",permalink:"/portfolio/blog/tags/data-storage"},{label:"Spark",permalink:"/portfolio/blog/tags/spark"},{label:"Pandas Dataframe",permalink:"/portfolio/blog/tags/pandas-dataframe"}],readingTime:3.92,hasTruncateMarker:!0,authors:[{name:"Morteza Hosseini",title:"Data scientist / ML engineer",key:"mori"}],frontMatter:{title:"Why is Parquet format so popular?",authors:["mori"],tags:["Data Science","Parquet","Data Storage","Spark","Pandas Dataframe"]},unlisted:!1,prevItem:{title:"Introducing Keras Core",permalink:"/portfolio/blog/keras-core"},nextItem:{title:"Venn Diagrams: Art & Pitfalls",permalink:"/portfolio/blog/venn"}},d={authorsImageUrls:[void 0]},c=[{value:"Introduction",id:"introduction",level:2},{value:"History",id:"history",level:2},{value:"Why so popular?",id:"why-so-popular",level:2},{value:"Parquet vs pandas DataFrame",id:"parquet-vs-pandas-dataframe",level:2},{value:"Conclusion",id:"conclusion",level:2}];function l(e){const a={a:"a",h2:"h2",img:"img",p:"p",table:"table",tbody:"tbody",td:"td",th:"th",thead:"thead",tr:"tr",...(0,s.a)(),...e.components};return(0,r.jsxs)(r.Fragment,{children:[(0,r.jsx)(a.h2,{id:"introduction",children:"Introduction"}),"\n",(0,r.jsxs)(a.p,{children:[(0,r.jsx)(a.a,{href:"https://parquet.apache.org/",children:"Parquet"})," is a popular columnar storage format for big data processing. It\u2019s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we\u2019ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we\u2019ll compare it to the popular pandas DataFrame."]}),"\n",(0,r.jsx)(a.h2,{id:"history",children:"History"}),"\n",(0,r.jsx)(a.p,{children:"The Parquet format was created in 2013 by the Apache Software Foundation\u2019s Parquet project, a collaboration between Twitter, Cloudera, and other organizations. The goal of the project was to create a columnar storage format that was optimized for big data processing and could be used with a variety of data processing frameworks such as Hadoop, Impala, and Hive. The project was a response to the growing need for a more efficient way of storing and processing large datasets as data collection and storage was rapidly increasing. Since its release, the Parquet format has become one of the most popular storage formats for big data, widely used in the industry and adopted by many companies."}),"\n",(0,r.jsx)(a.h2,{id:"why-so-popular",children:"Why so popular?"}),"\n",(0,r.jsx)(a.p,{children:"The first reason why Parquet is so popular is its high compression and encoding capabilities. Parquet uses a technique called columnar storage, which organizes data in a way that allows for more efficient compression and encoding. This means that data stored in Parquet format takes up less space on disk and can be read and processed more quickly."}),"\n",(0,r.jsx)(a.p,{children:"Another benefit of Parquet is its support for advanced data types and encoding schemes. Parquet supports a wide range of data types, including integers, floating-point numbers, strings, and timestamps. It also supports advanced encoding schemes like dictionary encoding, which can further reduce the size of the data on disk."}),"\n",(0,r.jsx)(a.p,{children:"Parquet also supports advanced data querying capabilities. It provides a feature called predicate pushdown, which allows query engines to filter data on disk before reading it into memory. This reduces the amount of data that needs to be read and processed, and can greatly improve query performance."}),"\n",(0,r.jsx)(a.p,{children:"In addition to its high compression and encoding capabilities, Parquet is also highly interoperable. It\u2019s supported by a wide range of big data processing tools and platforms, including Hadoop, Spark, Hive, and Pig. This means that data stored in Parquet format can be easily read and processed by a wide range of tools and platforms, making it a versatile format for big data processing tasks."}),"\n",(0,r.jsx)(a.p,{children:"Finally, Parquet is an open-source format, which means that it can be freely used and modified by anyone. This has led to a large and active community of developers working on the format, which has helped to improve its performance and capabilities over time."}),"\n",(0,r.jsx)(a.h2,{id:"parquet-vs-pandas-dataframe",children:"Parquet vs pandas DataFrame"}),"\n",(0,r.jsxs)(a.table,{children:[(0,r.jsx)(a.thead,{children:(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.th,{children:"Feature"}),(0,r.jsx)(a.th,{children:"Parquet"}),(0,r.jsx)(a.th,{children:"pandas DataFrames"})]})}),(0,r.jsxs)(a.tbody,{children:[(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.td,{children:"Storage format"}),(0,r.jsx)(a.td,{children:"Columnar"}),(0,r.jsx)(a.td,{children:"Row-based"})]}),(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.td,{children:"Compression"}),(0,r.jsx)(a.td,{children:"High"}),(0,r.jsx)(a.td,{children:"Low"})]}),(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.td,{children:"Encoding"}),(0,r.jsx)(a.td,{children:"Advanced (dictionary, RLE, etc.)"}),(0,r.jsx)(a.td,{children:"Basic"})]}),(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.td,{children:"Data querying"}),(0,r.jsx)(a.td,{children:"Advanced (predicate pushdown)"}),(0,r.jsx)(a.td,{children:"Basic"})]}),(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.td,{children:"Handling large data"}),(0,r.jsx)(a.td,{children:"Efficiently handle large data"}),(0,r.jsx)(a.td,{children:"Not optimized for large data processing"})]}),(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.td,{children:"Execution"}),(0,r.jsx)(a.td,{children:"Can be executed in distributed systems"}),(0,r.jsx)(a.td,{children:"Executed on single machine"})]}),(0,r.jsxs)(a.tr,{children:[(0,r.jsx)(a.td,{children:"Open-source"}),(0,r.jsx)(a.td,{children:"Yes"}),(0,r.jsx)(a.td,{children:"Yes"})]})]})]}),"\n",(0,r.jsxs)(a.p,{children:["As you can see, Parquet and ",(0,r.jsx)(a.a,{href:"https://pandas.pydata.org/",children:"pandas"})," DataFrames have some similarities, but they also have some important differences. Parquet is a columnar storage format that is optimized for big data processing and provides advanced compression and encoding capabilities. On the other hand, pandas DataFrames are row-based and are more commonly used for data analysis and manipulation."]}),"\n",(0,r.jsx)(a.p,{children:"One of the main benefits of using Parquet is that it can greatly improve the performance and efficiency of big data processing tasks by reducing the amount of data that needs to be read and processed. pandas DataFrames, on the other hand, are not as optimized for big data processing and may not be as efficient when working with large datasets."}),"\n",(0,r.jsxs)(a.p,{children:[(0,r.jsx)(a.img,{src:t(9979).Z+"",width:"1400",height:"1372"}),"\n",(0,r.jsx)(a.a,{href:"https://timepasstechies.com/row-oriented-column-oriented-file-formats-hadoop/",children:"https://timepasstechies.com/row-oriented-column-oriented-file-formats-hadoop/"})]}),"\n",(0,r.jsx)(a.p,{children:"Another benefit of Parquet is that it supports a wide range of data types and encoding schemes, which can further reduce the size of the data on disk. pandas DataFrames, on the other hand, have a limited set of data types and do not support advanced encoding schemes."}),"\n",(0,r.jsx)(a.h2,{id:"conclusion",children:"Conclusion"}),"\n",(0,r.jsx)(a.p,{children:"Parquet is a popular columnar storage format for big data processing due to its high compression and encoding capabilities, support for advanced data types and encoding schemes, advanced data querying capabilities, high interoperability, and the fact that it is open-source. These benefits make it a powerful and versatile format for big data processing tasks, and it\u2019s likely to continue to be widely used in the future."})]})}function h(e={}){const{wrapper:a}={...(0,s.a)(),...e.components};return a?(0,r.jsx)(a,{...e,children:(0,r.jsx)(l,{...e})}):l(e)}},9979:(e,a,t)=>{t.d(a,{Z:()=>r});const r=t.p+"assets/images/parquet-row-col-dc910928bf3092a41c8857c877007e38.jpeg"},11151:(e,a,t)=>{t.d(a,{Z:()=>i,a:()=>o});var r=t(67294);const s={},n=r.createContext(s);function o(e){const a=r.useContext(n);return r.useMemo((function(){return"function"==typeof e?e(a):{...a,...e}}),[a,e])}function i(e){let a;return a=e.disableParentContext?"function"==typeof e.components?e.components(s):e.components||s:o(e.components),r.createElement(n.Provider,{value:a},e.children)}}}]);
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diff --git a/assets/js/f1e3c45a.fc39feee.js b/assets/js/f1e3c45a.5e0349f8.js
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index acc3546d..65a9da71 100644
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+"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[6612],{33985:(e,t,a)=>{a.r(t),a.d(t,{assets:()=>r,contentTitle:()=>s,default:()=>m,frontMatter:()=>o,metadata:()=>l,toc:()=>c});var i=a(85893),n=a(11151);const o={title:"Venn Diagrams: Art & Pitfalls",authors:["mori"],tags:["Data Science","Data Visualization","Venn Diagrams","Pitfalls","Misinterpretations"]},s=void 0,l={permalink:"/portfolio/blog/venn",source:"@site/blog/venn.md",title:"Venn Diagrams: Art & Pitfalls",description:"In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I\u2019ve encountered their allure and their limitations. In this article, we\u2019ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We\u2019ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.",date:"2024-04-09T10:32:08.000Z",formattedDate:"April 9, 2024",tags:[{label:"Data Science",permalink:"/portfolio/blog/tags/data-science"},{label:"Data Visualization",permalink:"/portfolio/blog/tags/data-visualization"},{label:"Venn Diagrams",permalink:"/portfolio/blog/tags/venn-diagrams"},{label:"Pitfalls",permalink:"/portfolio/blog/tags/pitfalls"},{label:"Misinterpretations",permalink:"/portfolio/blog/tags/misinterpretations"}],readingTime:4.55,hasTruncateMarker:!0,authors:[{name:"Morteza Hosseini",title:"Data scientist / ML engineer",key:"mori"}],frontMatter:{title:"Venn Diagrams: Art & Pitfalls",authors:["mori"],tags:["Data Science","Data Visualization","Venn Diagrams","Pitfalls","Misinterpretations"]},unlisted:!1,prevItem:{title:"Why is Parquet format so popular?",permalink:"/portfolio/blog/parquet"}},r={authorsImageUrls:[void 0]},c=[];function p(e){const t={p:"p",...(0,n.a)(),...e.components};return(0,i.jsx)(t.p,{children:"In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I\u2019ve encountered their allure and their limitations. In this article, we\u2019ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We\u2019ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."})}function m(e={}){const{wrapper:t}={...(0,n.a)(),...e.components};return t?(0,i.jsx)(t,{...e,children:(0,i.jsx)(p,{...e})}):p(e)}},11151:(e,t,a)=>{a.d(t,{Z:()=>l,a:()=>s});var i=a(67294);const n={},o=i.createContext(n);function s(e){const t=i.useContext(o);return i.useMemo((function(){return"function"==typeof e?e(t):{...t,...e}}),[t,e])}function l(e){let t;return t=e.disableParentContext?"function"==typeof e.components?e.components(n):e.components||n:s(e.components),i.createElement(o.Provider,{value:t},e.children)}}}]);
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diff --git a/assets/js/f1ec2a79.084be4c3.js b/assets/js/f1ec2a79.39f7e9b5.js
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rename from assets/js/f1ec2a79.084be4c3.js
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index a40678ee..a845fe72 100644
--- a/assets/js/f1ec2a79.084be4c3.js
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-"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[8473],{91435:(e,i,a)=>{a.r(i),a.d(i,{assets:()=>l,contentTitle:()=>r,default:()=>h,frontMatter:()=>s,metadata:()=>o,toc:()=>c});var n=a(85893),t=a(11151);const s={title:"Exploring AI Ethics: 15 Key Q&As",authors:["mori"],tags:["AI","Ethics","Bias","Transparency","Privacy"]},r=void 0,o={permalink:"/portfolio/blog/ethics",source:"@site/blog/ethics.md",title:"Exploring AI Ethics: 15 Key Q&As",description:"Bias and Fairness in AI",date:"2024-04-05T14:49:00.000Z",formattedDate:"April 5, 2024",tags:[{label:"AI",permalink:"/portfolio/blog/tags/ai"},{label:"Ethics",permalink:"/portfolio/blog/tags/ethics"},{label:"Bias",permalink:"/portfolio/blog/tags/bias"},{label:"Transparency",permalink:"/portfolio/blog/tags/transparency"},{label:"Privacy",permalink:"/portfolio/blog/tags/privacy"}],readingTime:15.95,hasTruncateMarker:!0,authors:[{name:"Morteza Hosseini",title:"Data scientist / ML 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overcome?",id:"q6-what-challenges-can-arise-when-implementing-transparency-and-explainability-in-ai-systems-and-how-can-they-be-overcome",level:3},{value:"Data Privacy and Security",id:"data-privacy-and-security",level:2},{value:"Q7: Why is data privacy and security a significant concern in the context of AI, and what are the potential risks of neglecting these aspects?",id:"q7-why-is-data-privacy-and-security-a-significant-concern-in-the-context-of-ai-and-what-are-the-potential-risks-of-neglecting-these-aspects",level:3},{value:"Q8: How can data scientists and engineers ensure robust data privacy and security in AI projects, especially when working with sensitive data?",id:"q8-how-can-data-scientists-and-engineers-ensure-robust-data-privacy-and-security-in-ai-projects-especially-when-working-with-sensitive-data",level:3},{value:"Q9: How can organizations balance data utilization for AI advancement with respecting individual data privacy rights?",id:"q9-how-can-organizations-balance-data-utilization-for-ai-advancement-with-respecting-individual-data-privacy-rights",level:3},{value:"Accountability and Legal Frameworks",id:"accountability-and-legal-frameworks",level:2},{value:"Q10: Why is establishing accountability in AI crucial, and how does it relate to ethical considerations?",id:"q10-why-is-establishing-accountability-in-ai-crucial-and-how-does-it-relate-to-ethical-considerations",level:3},{value:"Q11: How can legal frameworks address AI challenges, and what\u2019s the role of international collaboration?",id:"q11-how-can-legal-frameworks-address-ai-challenges-and-whats-the-role-of-international-collaboration",level:3},{value:"Q12: How can organizations and AI practitioners proactively integrate accountability into their AI development processes?",id:"q12-how-can-organizations-and-ai-practitioners-proactively-integrate-accountability-into-their-ai-development-processes",level:3},{value:"Ethical Considerations in AI R&amp;D",id:"ethical-considerations-in-ai-rd",level:2},{value:"Q13: Why must ethical considerations be part of AI R&amp;D from the project\u2019s outset?",id:"q13-why-must-ethical-considerations-be-part-of-ai-rd-from-the-projects-outset",level:3},{value:"Q14: How can researchers handle ethics in AI development with emerging tech like deep learning?",id:"q14-how-can-researchers-handle-ethics-in-ai-development-with-emerging-tech-like-deep-learning",level:3},{value:"Q15: How can researchers and engineers balance the pursuit of technological innovation in AI R&amp;D with ethical considerations?",id:"q15-how-can-researchers-and-engineers-balance-the-pursuit-of-technological-innovation-in-ai-rd-with-ethical-considerations",level:3}];function d(e){const i={a:"a",em:"em",h2:"h2",h3:"h3",p:"p",...(0,t.a)(),...e.components};return(0,n.jsxs)(n.Fragment,{children:[(0,n.jsx)(i.h2,{id:"bias-and-fairness-in-ai",children:"Bias and Fairness in AI"}),"\n",(0,n.jsx)(i.p,{children:"In the realm of artificial intelligence, addressing the issue of bias and ensuring fairness in algorithms is paramount. AI systems have the potential to perpetuate and even amplify societal biases if not carefully designed and monitored. Exploring this topic delves into the ethical responsibility of AI researchers and practitioners to mitigate biases and promote equity in AI applications."}),"\n",(0,n.jsx)(i.h3,{id:"q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications",children:"Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?"}),"\n",(0,n.jsxs)(i.p,{children:["Addressing bias and ensuring fairness in AI is of paramount importance in real-world applications because these technologies are increasingly integrated into ",(0,n.jsx)(i.em,{children:"decision-making"})," processes across various domains, from finance and healthcare to criminal justice. When AI models exhibit bias, they can perpetuate and even exacerbate ",(0,n.jsx)(i.em,{children:"societal inequalities"}),". For example, biased algorithms in lending can lead to discrimination against marginalized groups, and biased facial recognition systems may lead to wrongful arrests. These issues not only ",(0,n.jsx)(i.em,{children:"undermine trust in AI"})," but also have real-life consequences for individuals and communities. Thus, addressing bias and ensuring fairness is an ethical imperative, promoting equity, and mitigating the harmful impact of AI on vulnerable populations."]}),"\n",(0,n.jsx)(i.p,{children:"Additionally, from a business perspective, failing to address bias and fairness concerns can result in legal and reputational risks. Companies that deploy biased AI systems may face lawsuits and damage to their brand image. Therefore, it\u2019s not only a moral obligation but also a strategic imperative for organizations to invest in addressing bias and ensuring fairness in their AI technologies. Ultimately, by actively working to eliminate bias and enhance fairness, we can harness the potential of AI for the betterment of society and avoid reinforcing existing inequalities."}),"\n",(0,n.jsx)(i.h3,{id:"q2-how-can-data-scientists-proactively-tackle-bias-in-ai-systems-during-the-development-process",children:"Q2: How can data scientists proactively tackle bias in AI systems during the development process?"}),"\n",(0,n.jsxs)(i.p,{children:["Data scientists can proactively tackle bias in AI systems during the development process through a multi-faceted approach. Firstly, they should conduct rigorous ",(0,n.jsx)(i.em,{children:"data preprocessing"})," to identify and mitigate bias in training data. This involves not only using representative datasets but also carefully examining historical data for any existing biases. Additionally, it\u2019s crucial to involve diverse teams in the development process to bring different perspectives and ensure comprehensive bias detection."]}),"\n",(0,n.jsx)(i.p,{children:"Furthermore, transparency and explainability play a pivotal role. Data scientists should select interpretable algorithms and develop model-agnostic methods for explaining AI decisions. This not only aids in identifying bias but also helps build trust by allowing end-users to understand how decisions are made. Regularly auditing and re-evaluating AI systems post-deployment is also vital to ensure ongoing fairness and address emerging biases as they arise. Lastly, ethical considerations should be embedded into the design process, including defining clear fairness metrics and establishing guidelines for addressing bias. By integrating these strategies into the AI development lifecycle, data scientists and engineers can work towards creating more ethical and fair AI systems."}),"\n",(0,n.jsx)(i.h3,{id:"q3-what-role-do-regulatory-frameworks-and-industry-standards-play-in-mitigating-bias-and-promoting-fairness-in-ai",children:"Q3: What role do regulatory frameworks and industry standards play in mitigating bias and promoting fairness in AI?"}),"\n",(0,n.jsxs)(i.p,{children:["Regulatory frameworks and industry standards play a crucial role in mitigating bias and promoting fairness in AI. Governments and organizations worldwide are recognizing the ethical challenges posed by AI, and as a result, they are developing guidelines and regulations to address these concerns. For instance, the European Union\u2019s General Data Protection Regulation (",(0,n.jsx)(i.a,{href:"https://gdpr-info.eu/",children:"GDPR"}),") includes provisions related to automated decision-making, which impacts AI systems. Similarly, the United States is considering legislation aimed at improving AI transparency and accountability (see e.g. ",(0,n.jsx)(i.a,{href:"https://oag.ca.gov/privacy/ccpa",children:"CCPA"}),"). These regulations often require organizations to be transparent about their AI systems, conduct ",(0,n.jsx)(i.em,{children:"bias audits"}),", and ensure fairness in decision-making processes. By imposing legal obligations, such frameworks incentivize companies to prioritize fairness in AI development and reduce the risk of discriminatory outcomes. Furthermore, industry standards, like those developed by organizations such as IEEE or the Partnership on AI, provide best practices and guidelines for ethical AI development, serving as a valuable resource for data scientists and engineers seeking to address bias and promote fairness."]}),"\n",(0,n.jsx)(i.h2,{id:"transparency-and-explainability",children:"Transparency and Explainability"}),"\n",(0,n.jsx)(i.p,{children:"The increasing complexity of AI models raises fundamental questions about transparency and explainability. As AI becomes more integrated into our lives, it\u2019s essential to understand how these systems arrive at their decisions. This category examines the ethical imperative of making AI processes understandable and interpretable, fostering trust between technology and society."}),"\n",(0,n.jsx)(i.h3,{id:"q4-why-is-transparency-and-explainability-essential-in-the-development-and-deployment-of-ai-systems",children:"Q4: Why is transparency and explainability essential in the development and deployment of AI systems?"}),"\n",(0,n.jsxs)(i.p,{children:["Transparency and explainability are essential in the development and deployment of AI systems for several critical reasons. Firstly, they foster trust and accountability. When individuals can understand how AI systems arrive at their decisions, it becomes easier to ",(0,n.jsx)(i.em,{children:"trust and validate those decisions"}),". This is especially important in high-stakes applications like healthcare or autonomous vehicles, where transparency can be a matter of life and death. Secondly, transparency helps uncover biases and errors within AI models. By providing insight into the decision-making process, it allows data scientists and engineers to identify and correct problematic patterns in the data or algorithms. Thirdly, from a regulatory perspective, many countries are introducing laws and regulations that require explanations for automated decisions. Compliance with these regulations is crucial to avoid legal repercussions. Overall, transparency and explainability are not just ethical imperatives but also practical necessities for the responsible development and deployment of AI technology."]}),"\n",(0,n.jsx)(i.h3,{id:"q5-how-can-data-practitioners-ensure-transparency-and-explainability-in-complex-deep-learning-models-often-considered-black-boxes",children:"Q5: How can data practitioners ensure transparency and explainability in complex deep learning models, often considered \u201cblack boxes\u201d?"}),"\n",(0,n.jsxs)(i.p,{children:["Ensuring transparency and explainability in complex deep learning models, often regarded as \u201cblack boxes,\u201d is indeed a challenging task. However, it\u2019s not impossible, and there are several strategies that data scientists and engineers can employ. Firstly, they can use ",(0,n.jsx)(i.em,{children:"model-agnostic techniques for interpretability"}),". These methods, such as ",(0,n.jsx)(i.a,{href:"https://github.com/marcotcr/lime",children:"LIME"})," (Local Interpretable Model-agnostic Explanations) or ",(0,n.jsx)(i.a,{href:"https://github.com/shap/shap",children:"SHAP"})," (SHapley Additive exPlanations), can provide insights into the model\u2019s decision-making process without needing to fully understand the complex neural network. Secondly, researchers are actively working on developing more interpretable deep learning architectures. These models are designed with transparency and explainability in mind and can provide clearer insights into the features and patterns the model is using to make decisions. Additionally, creating transparency in the data pipeline is crucial. Documenting data sources, preprocessing steps, and feature engineering can help explain why certain decisions were made based on the input data. Lastly, adopting industry standards and best practices for transparency can guide data scientists and engineers in their efforts to make AI more transparent and explainable, even in complex deep learning scenarios."]}),"\n",(0,n.jsx)(i.h3,{id:"q6-what-challenges-can-arise-when-implementing-transparency-and-explainability-in-ai-systems-and-how-can-they-be-overcome",children:"Q6: What challenges can arise when implementing transparency and explainability in AI systems, and how can they be overcome?"}),"\n",(0,n.jsxs)(i.p,{children:["Implementing transparency and explainability in AI systems can pose several challenges. One common challenge is the ",(0,n.jsx)(i.em,{children:"trade-off between model complexity and interpretability"}),". Deep learning models, for example, can be highly accurate but difficult to interpret. To overcome this, data scientists must strike a balance between model performance and the need for transparency. Additionally, ensuring real-time explainability in applications like autonomous vehicles or medical diagnosis can be challenging, as explanations need to be provided instantly. Addressing this challenge requires the development of efficient and fast-explaining techniques. Furthermore, as AI systems evolve and adapt, maintaining transparency becomes an ongoing challenge. Data scientists need to implement continuous ",(0,n.jsx)(i.em,{children:"monitoring"})," and auditing processes to ensure that models remain transparent and explainable as they are updated. Finally, there may be resistance from organizations or stakeholders who are concerned that transparency could reveal proprietary or sensitive information. Addressing this challenge involves finding ways to provide explanations without divulging confidential data, possibly through the use of ",(0,n.jsx)(i.em,{children:"privacy-preserving techniques"})," or summarized explanations."]}),"\n",(0,n.jsx)(i.h2,{id:"data-privacy-and-security",children:"Data Privacy and Security"}),"\n",(0,n.jsx)(i.p,{children:"Protecting data privacy and ensuring security in AI applications is an ethical obligation that AI practitioners must prioritize. As we leverage vast amounts of personal data to train AI models, the potential for misuse and breaches grows. This topic dives into the critical need to establish robust safeguards and ethical practices to safeguard individuals\u2019 privacy and protect against data breaches."}),"\n",(0,n.jsx)(i.h3,{id:"q7-why-is-data-privacy-and-security-a-significant-concern-in-the-context-of-ai-and-what-are-the-potential-risks-of-neglecting-these-aspects",children:"Q7: Why is data privacy and security a significant concern in the context of AI, and what are the potential risks of neglecting these aspects?"}),"\n",(0,n.jsxs)(i.p,{children:["Data privacy and security are significant concerns in the context of AI because AI systems rely heavily on large and often sensitive datasets. Neglecting these aspects can lead to several potential risks. Firstly, there\u2019s the ",(0,n.jsx)(i.em,{children:"risk of unauthorized access"})," and data breaches, which can result in the exposure of personal information, financial records, or other sensitive data, leading to identity theft, financial fraud, or privacy violations. Secondly, when AI models are trained on data that contains personally identifiable information (",(0,n.jsx)(i.a,{href:"https://en.wikipedia.org/wiki/Personal_data",children:"PII"}),"), there\u2019s a risk of unintentional disclosure of private information during the model\u2019s operation, especially if it\u2019s used in applications like healthcare or finance. Additionally, the misuse of AI technology, if data privacy and security measures are not in place, can result in discriminatory practices, reinforcing existing biases, and harming vulnerable populations. Finally, from a regulatory perspective, non-compliance with data protection laws, such as GDPR or CCPA, can result in significant legal and financial penalties. Therefore, prioritizing data privacy and security in AI is not only an ethical imperative but also a legal and reputational necessity."]}),"\n",(0,n.jsx)(i.h3,{id:"q8-how-can-data-scientists-and-engineers-ensure-robust-data-privacy-and-security-in-ai-projects-especially-when-working-with-sensitive-data",children:"Q8: How can data scientists and engineers ensure robust data privacy and security in AI projects, especially when working with sensitive data?"}),"\n",(0,n.jsxs)(i.p,{children:["Ensuring robust data privacy and security in AI projects, especially when dealing with sensitive data, requires a comprehensive approach. Firstly, data scientists and engineers should implement strong ",(0,n.jsx)(i.em,{children:"data anonymization"})," and ",(0,n.jsx)(i.em,{children:"encryption"})," techniques during data collection, storage, and transmission. This prevents unauthorized access and protects the confidentiality of the data. Secondly, access control mechanisms must be in place to restrict who can access and modify the data and AI models. ",(0,n.jsx)(i.em,{children:"Role-based access control"})," and authentication methods can be effective in this regard. Thirdly, ",(0,n.jsx)(i.em,{children:"data minimization"})," should be a guiding principle. Collect only the data necessary for the specific AI task, reducing the risk associated with excessive data exposure. Additionally, regular security audits and vulnerability assessments should be conducted to identify and address potential weaknesses in the AI system. Collaboration with cybersecurity experts is crucial to stay ahead of evolving threats.\nFurthermore, compliance with data protection regulations should be a priority. Data scientists and engineers should be well-versed in the legal requirements of the regions where their AI systems will operate and ensure that their projects adhere to these standards. Finally, ongoing monitoring and incident response plans should be established to swiftly detect and address any security breaches or privacy violations. By adopting these practices, data scientists and engineers can significantly enhance data privacy and security in AI projects, safeguarding both individuals\u2019 sensitive information and the integrity of AI applications."]}),"\n",(0,n.jsx)(i.h3,{id:"q9-how-can-organizations-balance-data-utilization-for-ai-advancement-with-respecting-individual-data-privacy-rights",children:"Q9: How can organizations balance data utilization for AI advancement with respecting individual data privacy rights?"}),"\n",(0,n.jsxs)(i.p,{children:["Striking a balance between utilizing valuable data for AI advancements and respecting individual data privacy rights is a delicate but essential task. Organizations can begin by implementing ",(0,n.jsx)(i.em,{children:"data anonymization"})," techniques that allow them to use data for AI training and analytics without exposing individuals\u2019 identities. This approach ensures that the data retains its value for insights while minimizing the risks to privacy. Additionally, adopting the principle of ",(0,n.jsx)(i.em,{children:"data minimization"})," is crucial. Collect and store only the data necessary for specific AI tasks, reducing the amount of potentially sensitive information at risk. Moreover, organizations can prioritize transparency by clearly communicating their data usage and privacy policies to users. Providing individuals with informed consent and opt-in choices empowers them to make decisions about how their data is used in AI applications. Lastly, investing in state-of-the-art security measures and compliance with data protection regulations is non-negotiable. This not only safeguards data but also ensures organizations are legally compliant, thereby mitigating risks associated with data breaches and privacy violations. In essence, finding the right balance between data utilization and privacy protection involves a combination of technical, ethical, and legal strategies."]}),"\n",(0,n.jsx)(i.h2,{id:"accountability-and-legal-frameworks",children:"Accountability and Legal Frameworks"}),"\n",(0,n.jsx)(i.p,{children:"Accountability is a central concern in the AI landscape. Determining who is responsible when AI systems fail or cause harm is a complex ethical challenge. This topic explores the development of legal frameworks and ethical standards to hold both individuals and organizations accountable for the consequences of AI technology."}),"\n",(0,n.jsx)(i.h3,{id:"q10-why-is-establishing-accountability-in-ai-crucial-and-how-does-it-relate-to-ethical-considerations",children:"Q10: Why is establishing accountability in AI crucial, and how does it relate to ethical considerations?"}),"\n",(0,n.jsxs)(i.p,{children:["Establishing accountability in AI is crucial because it ensures that individuals and organizations are held ",(0,n.jsx)(i.em,{children:"responsible"})," for the consequences of AI system behavior. This accountability is directly linked to ethical considerations as it promotes transparency, fairness, and the responsible use of AI technology. When accountability is established, it becomes clear who is responsible for designing, developing, and deploying AI systems. This clarity is essential for addressing issues such as bias, discrimination, and harm caused by AI. Ethical considerations demand that those who create and operate AI systems are aware of their ethical obligations and take steps to prevent negative outcomes. Without accountability, it\u2019s challenging to enforce ethical standards and ensure that AI systems serve the best interests of society as a whole."]}),"\n",(0,n.jsx)(i.h3,{id:"q11-how-can-legal-frameworks-address-ai-challenges-and-whats-the-role-of-international-collaboration",children:"Q11: How can legal frameworks address AI challenges, and what\u2019s the role of international collaboration?"}),"\n",(0,n.jsxs)(i.p,{children:["Legal frameworks play a vital role in addressing the complex and evolving challenges posed by AI. These frameworks can provide clear guidelines, standards, and ",(0,n.jsx)(i.em,{children:"enforcement mechanisms"})," for ethical AI development and usage. To effectively address AI-related challenges, legal frameworks should be adaptable and capable of keeping pace with rapidly evolving technology. They should cover various aspects, including data protection, transparency, accountability, and liability. Additionally, international collaborations are essential in this context. AI does not respect national borders, and many AI applications are developed and deployed globally. Collaborative efforts between countries can help harmonize AI regulations, making it easier for organizations to navigate the legal landscape. Organizations like the United Nations and regional bodies such as the European Union have started working on frameworks and guidelines to address AI ethics and governance on a global scale. These collaborations facilitate the sharing of best practices and help establish a unified approach to AI ethics, ultimately fostering a more responsible and accountable AI ecosystem."]}),"\n",(0,n.jsx)(i.h3,{id:"q12-how-can-organizations-and-ai-practitioners-proactively-integrate-accountability-into-their-ai-development-processes",children:"Q12: How can organizations and AI practitioners proactively integrate accountability into their AI development processes?"}),"\n",(0,n.jsxs)(i.p,{children:["Organizations and AI practitioners can proactively integrate accountability into their AI development processes by adopting a holistic approach that encompasses several key steps. First and foremost, they should establish clear lines of responsibility within their teams. This includes designating individuals or teams accountable for AI ethics and compliance, ensuring that ethical considerations are integrated from the project\u2019s inception. Additionally, they should conduct comprehensive ",(0,n.jsx)(i.em,{children:"ethical impact assessments"})," throughout the AI development lifecycle, identifying potential risks and biases and taking steps to mitigate them. This may involve using fairness-aware algorithms, data audits, and bias mitigation techniques."]}),"\n",(0,n.jsxs)(i.p,{children:["Furthermore, organizations can foster a culture of ethical AI by providing training and awareness programs for their employees, promoting ethical decision-making, and incentivizing responsible AI practices. ",(0,n.jsx)(i.em,{children:"Regularly monitoring"})," AI systems post-deployment and collecting user feedback can help identify and rectify ethical concerns as they arise. Lastly, organizations should be prepared to engage with external stakeholders, including regulatory bodies, to demonstrate their commitment to accountability and compliance with legal frameworks. By integrating accountability at every stage of AI development, organizations and practitioners can ensure that ethical considerations are not an ",(0,n.jsx)(i.em,{children:"afterthought"})," but a fundamental part of their AI processes."]}),"\n",(0,n.jsx)(i.h2,{id:"ethical-considerations-in-ai-rd",children:"Ethical Considerations in AI R&D"}),"\n",(0,n.jsx)(i.p,{children:"The research and development of AI technologies come with their own set of ethical considerations. These encompass the choices made during the design and creation of AI systems, from data collection methods to algorithmic decision-making. Examining this topic sheds light on the ethical responsibilities that researchers and engineers bear throughout the AI development lifecycle."}),"\n",(0,n.jsx)(i.h3,{id:"q13-why-must-ethical-considerations-be-part-of-ai-rd-from-the-projects-outset",children:"Q13: Why must ethical considerations be part of AI R&D from the project\u2019s outset?"}),"\n",(0,n.jsxs)(i.p,{children:["It\u2019s crucial for researchers and engineers to consider ethical implications during the early stages of AI R&D because ethical considerations are intertwined with the entire AI development process. Neglecting ethics in the early stages can lead to unintended consequences down the line. Ethical issues, such as bias or privacy concerns, often originate from decisions made in data collection, algorithm design, and model training. By addressing these concerns at the outset, researchers can mitigate potential harm and bias. Additionally, ethical considerations are essential for ",(0,n.jsx)(i.em,{children:"building trust with users"})," and stakeholders. Users are more likely to adopt and trust AI systems that have been developed with ethics in mind. Furthermore, considering ethics from the beginning helps prevent costly rework and legal issues that may arise if ethical concerns are only addressed after an AI system has been deployed. In essence, incorporating ethics into AI R&D from the start is not only responsible but also practical for ensuring the long-term success and impact of AI projects."]}),"\n",(0,n.jsx)(i.h3,{id:"q14-how-can-researchers-handle-ethics-in-ai-development-with-emerging-tech-like-deep-learning",children:"Q14: How can researchers handle ethics in AI development with emerging tech like deep learning?"}),"\n",(0,n.jsxs)(i.p,{children:["Navigating the ethical challenges associated with emerging technologies like deep learning and reinforcement learning requires a proactive and multidimensional approach. Firstly, researchers and engineers should prioritize ",(0,n.jsx)(i.em,{children:"transparency"})," and ",(0,n.jsx)(i.em,{children:"explainability"}),", even in complex models. While deep learning models can be challenging to interpret, efforts should be made to make their decisions understandable. Model-agnostic techniques and interpretability tools can help provide insights into the inner workings of these models. Secondly, they should be aware of the potential for unintended biases to emerge in the data used to train these models. Careful data preprocessing, bias audits, and diverse datasets can help mitigate this risk. Thirdly, involving multidisciplinary teams is essential. Ethicists, social scientists, and domain experts can provide valuable perspectives on the ethical implications of AI technologies and help shape responsible development. Moreover, staying informed about emerging ethical guidelines and standards in the AI field is vital, as these guidelines evolve alongside technology. Finally, considering the long-term impact of AI systems and their potential societal consequences is essential when working with emerging technologies. Researchers and engineers should ask critical questions about the implications of their work and anticipate potential ethical dilemmas, aiming for solutions that prioritize fairness, transparency, and the well-being of society as a whole."]}),"\n",(0,n.jsx)(i.h3,{id:"q15-how-can-researchers-and-engineers-balance-the-pursuit-of-technological-innovation-in-ai-rd-with-ethical-considerations",children:"Q15: How can researchers and engineers balance the pursuit of technological innovation in AI R&D with ethical considerations?"}),"\n",(0,n.jsxs)(i.p,{children:["Balancing technological innovation with ethical considerations in AI R&D requires a mindful approach. Researchers and engineers can begin by setting clear ethical goals and principles at the project\u2019s inception. This involves ",(0,n.jsx)(i.em,{children:"defining ethical boundaries"})," and ensuring that innovation aligns with these principles. Regular ethical reviews and impact assessments can help identify potential conflicts between innovation and ethical considerations. Moreover, interdisciplinary collaboration is invaluable. By involving ethicists, social scientists, and experts from various fields, teams can benefit from diverse perspectives that help steer innovation in ethically responsible directions. Additionally, organizations can establish dedicated ",(0,n.jsx)(i.em,{children:"ethical review boards or committees"})," that evaluate projects from an ethical standpoint, providing guidance and ensuring that innovation aligns with ethical norms. Lastly, transparency is key. Researchers and engineers should communicate their ethical commitments and progress to stakeholders, including the public, to build trust and demonstrate a commitment to responsible innovation. Ultimately, ethical considerations and innovation are not mutually exclusive but should be mutually reinforcing, with ethical boundaries guiding and enhancing the impact of technological advances."]})]})}function h(e={}){const{wrapper:i}={...(0,t.a)(),...e.components};return i?(0,n.jsx)(i,{...e,children:(0,n.jsx)(d,{...e})}):d(e)}},11151:(e,i,a)=>{a.d(i,{Z:()=>o,a:()=>r});var n=a(67294);const t={},s=n.createContext(t);function r(e){const i=n.useContext(s);return n.useMemo((function(){return"function"==typeof e?e(i):{...i,...e}}),[i,e])}function o(e){let i;return i=e.disableParentContext?"function"==typeof e.components?e.components(t):e.components||t:r(e.components),n.createElement(s.Provider,{value:i},e.children)}}}]);
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engineer",key:"mori"}],frontMatter:{title:"Exploring AI Ethics: 15 Key Q&As",authors:["mori"],tags:["AI","Ethics","Bias","Transparency","Privacy"]},unlisted:!1,prevItem:{title:"Anonymization vs. Pseudonymization",permalink:"/portfolio/blog/anonym-pseudonym"},nextItem:{title:"Get-Rich-Quick Schemes are a Bad Idea",permalink:"/portfolio/blog/get-rich-quick"}},l={authorsImageUrls:[void 0]},c=[{value:"Bias and Fairness in AI",id:"bias-and-fairness-in-ai",level:2},{value:"Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?",id:"q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications",level:3},{value:"Q2: How can data scientists proactively tackle bias in AI systems during the development process?",id:"q2-how-can-data-scientists-proactively-tackle-bias-in-ai-systems-during-the-development-process",level:3},{value:"Q3: What role do regulatory frameworks and industry standards play in mitigating bias and promoting fairness in AI?",id:"q3-what-role-do-regulatory-frameworks-and-industry-standards-play-in-mitigating-bias-and-promoting-fairness-in-ai",level:3},{value:"Transparency and Explainability",id:"transparency-and-explainability",level:2},{value:"Q4: Why is transparency and explainability essential in the development and deployment of AI systems?",id:"q4-why-is-transparency-and-explainability-essential-in-the-development-and-deployment-of-ai-systems",level:3},{value:"Q5: How can data practitioners ensure transparency and explainability in complex deep learning models, often considered \u201cblack boxes\u201d?",id:"q5-how-can-data-practitioners-ensure-transparency-and-explainability-in-complex-deep-learning-models-often-considered-black-boxes",level:3},{value:"Q6: What challenges can arise when implementing transparency and explainability in AI systems, and how can they be overcome?",id:"q6-what-challenges-can-arise-when-implementing-transparency-and-explainability-in-ai-systems-and-how-can-they-be-overcome",level:3},{value:"Data Privacy and Security",id:"data-privacy-and-security",level:2},{value:"Q7: Why is data privacy and security a significant concern in the context of AI, and what are the potential risks of neglecting these aspects?",id:"q7-why-is-data-privacy-and-security-a-significant-concern-in-the-context-of-ai-and-what-are-the-potential-risks-of-neglecting-these-aspects",level:3},{value:"Q8: How can data scientists and engineers ensure robust data privacy and security in AI projects, especially when working with sensitive data?",id:"q8-how-can-data-scientists-and-engineers-ensure-robust-data-privacy-and-security-in-ai-projects-especially-when-working-with-sensitive-data",level:3},{value:"Q9: How can organizations balance data utilization for AI advancement with respecting individual data privacy rights?",id:"q9-how-can-organizations-balance-data-utilization-for-ai-advancement-with-respecting-individual-data-privacy-rights",level:3},{value:"Accountability and Legal Frameworks",id:"accountability-and-legal-frameworks",level:2},{value:"Q10: Why is establishing accountability in AI crucial, and how does it relate to ethical considerations?",id:"q10-why-is-establishing-accountability-in-ai-crucial-and-how-does-it-relate-to-ethical-considerations",level:3},{value:"Q11: How can legal frameworks address AI challenges, and what\u2019s the role of international collaboration?",id:"q11-how-can-legal-frameworks-address-ai-challenges-and-whats-the-role-of-international-collaboration",level:3},{value:"Q12: How can organizations and AI practitioners proactively integrate accountability into their AI development processes?",id:"q12-how-can-organizations-and-ai-practitioners-proactively-integrate-accountability-into-their-ai-development-processes",level:3},{value:"Ethical Considerations in AI R&amp;D",id:"ethical-considerations-in-ai-rd",level:2},{value:"Q13: Why must ethical considerations be part of AI R&amp;D from the project\u2019s outset?",id:"q13-why-must-ethical-considerations-be-part-of-ai-rd-from-the-projects-outset",level:3},{value:"Q14: How can researchers handle ethics in AI development with emerging tech like deep learning?",id:"q14-how-can-researchers-handle-ethics-in-ai-development-with-emerging-tech-like-deep-learning",level:3},{value:"Q15: How can researchers and engineers balance the pursuit of technological innovation in AI R&amp;D with ethical considerations?",id:"q15-how-can-researchers-and-engineers-balance-the-pursuit-of-technological-innovation-in-ai-rd-with-ethical-considerations",level:3}];function d(e){const i={a:"a",em:"em",h2:"h2",h3:"h3",p:"p",...(0,t.a)(),...e.components};return(0,n.jsxs)(n.Fragment,{children:[(0,n.jsx)(i.h2,{id:"bias-and-fairness-in-ai",children:"Bias and Fairness in AI"}),"\n",(0,n.jsx)(i.p,{children:"In the realm of artificial intelligence, addressing the issue of bias and ensuring fairness in algorithms is paramount. AI systems have the potential to perpetuate and even amplify societal biases if not carefully designed and monitored. Exploring this topic delves into the ethical responsibility of AI researchers and practitioners to mitigate biases and promote equity in AI applications."}),"\n",(0,n.jsx)(i.h3,{id:"q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications",children:"Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?"}),"\n",(0,n.jsxs)(i.p,{children:["Addressing bias and ensuring fairness in AI is of paramount importance in real-world applications because these technologies are increasingly integrated into ",(0,n.jsx)(i.em,{children:"decision-making"})," processes across various domains, from finance and healthcare to criminal justice. When AI models exhibit bias, they can perpetuate and even exacerbate ",(0,n.jsx)(i.em,{children:"societal inequalities"}),". For example, biased algorithms in lending can lead to discrimination against marginalized groups, and biased facial recognition systems may lead to wrongful arrests. These issues not only ",(0,n.jsx)(i.em,{children:"undermine trust in AI"})," but also have real-life consequences for individuals and communities. Thus, addressing bias and ensuring fairness is an ethical imperative, promoting equity, and mitigating the harmful impact of AI on vulnerable populations."]}),"\n",(0,n.jsx)(i.p,{children:"Additionally, from a business perspective, failing to address bias and fairness concerns can result in legal and reputational risks. Companies that deploy biased AI systems may face lawsuits and damage to their brand image. Therefore, it\u2019s not only a moral obligation but also a strategic imperative for organizations to invest in addressing bias and ensuring fairness in their AI technologies. Ultimately, by actively working to eliminate bias and enhance fairness, we can harness the potential of AI for the betterment of society and avoid reinforcing existing inequalities."}),"\n",(0,n.jsx)(i.h3,{id:"q2-how-can-data-scientists-proactively-tackle-bias-in-ai-systems-during-the-development-process",children:"Q2: How can data scientists proactively tackle bias in AI systems during the development process?"}),"\n",(0,n.jsxs)(i.p,{children:["Data scientists can proactively tackle bias in AI systems during the development process through a multi-faceted approach. Firstly, they should conduct rigorous ",(0,n.jsx)(i.em,{children:"data preprocessing"})," to identify and mitigate bias in training data. This involves not only using representative datasets but also carefully examining historical data for any existing biases. Additionally, it\u2019s crucial to involve diverse teams in the development process to bring different perspectives and ensure comprehensive bias detection."]}),"\n",(0,n.jsx)(i.p,{children:"Furthermore, transparency and explainability play a pivotal role. Data scientists should select interpretable algorithms and develop model-agnostic methods for explaining AI decisions. This not only aids in identifying bias but also helps build trust by allowing end-users to understand how decisions are made. Regularly auditing and re-evaluating AI systems post-deployment is also vital to ensure ongoing fairness and address emerging biases as they arise. Lastly, ethical considerations should be embedded into the design process, including defining clear fairness metrics and establishing guidelines for addressing bias. By integrating these strategies into the AI development lifecycle, data scientists and engineers can work towards creating more ethical and fair AI systems."}),"\n",(0,n.jsx)(i.h3,{id:"q3-what-role-do-regulatory-frameworks-and-industry-standards-play-in-mitigating-bias-and-promoting-fairness-in-ai",children:"Q3: What role do regulatory frameworks and industry standards play in mitigating bias and promoting fairness in AI?"}),"\n",(0,n.jsxs)(i.p,{children:["Regulatory frameworks and industry standards play a crucial role in mitigating bias and promoting fairness in AI. Governments and organizations worldwide are recognizing the ethical challenges posed by AI, and as a result, they are developing guidelines and regulations to address these concerns. For instance, the European Union\u2019s General Data Protection Regulation (",(0,n.jsx)(i.a,{href:"https://gdpr-info.eu/",children:"GDPR"}),") includes provisions related to automated decision-making, which impacts AI systems. Similarly, the United States is considering legislation aimed at improving AI transparency and accountability (see e.g. ",(0,n.jsx)(i.a,{href:"https://oag.ca.gov/privacy/ccpa",children:"CCPA"}),"). These regulations often require organizations to be transparent about their AI systems, conduct ",(0,n.jsx)(i.em,{children:"bias audits"}),", and ensure fairness in decision-making processes. By imposing legal obligations, such frameworks incentivize companies to prioritize fairness in AI development and reduce the risk of discriminatory outcomes. Furthermore, industry standards, like those developed by organizations such as IEEE or the Partnership on AI, provide best practices and guidelines for ethical AI development, serving as a valuable resource for data scientists and engineers seeking to address bias and promote fairness."]}),"\n",(0,n.jsx)(i.h2,{id:"transparency-and-explainability",children:"Transparency and Explainability"}),"\n",(0,n.jsx)(i.p,{children:"The increasing complexity of AI models raises fundamental questions about transparency and explainability. As AI becomes more integrated into our lives, it\u2019s essential to understand how these systems arrive at their decisions. This category examines the ethical imperative of making AI processes understandable and interpretable, fostering trust between technology and society."}),"\n",(0,n.jsx)(i.h3,{id:"q4-why-is-transparency-and-explainability-essential-in-the-development-and-deployment-of-ai-systems",children:"Q4: Why is transparency and explainability essential in the development and deployment of AI systems?"}),"\n",(0,n.jsxs)(i.p,{children:["Transparency and explainability are essential in the development and deployment of AI systems for several critical reasons. Firstly, they foster trust and accountability. When individuals can understand how AI systems arrive at their decisions, it becomes easier to ",(0,n.jsx)(i.em,{children:"trust and validate those decisions"}),". This is especially important in high-stakes applications like healthcare or autonomous vehicles, where transparency can be a matter of life and death. Secondly, transparency helps uncover biases and errors within AI models. By providing insight into the decision-making process, it allows data scientists and engineers to identify and correct problematic patterns in the data or algorithms. Thirdly, from a regulatory perspective, many countries are introducing laws and regulations that require explanations for automated decisions. Compliance with these regulations is crucial to avoid legal repercussions. Overall, transparency and explainability are not just ethical imperatives but also practical necessities for the responsible development and deployment of AI technology."]}),"\n",(0,n.jsx)(i.h3,{id:"q5-how-can-data-practitioners-ensure-transparency-and-explainability-in-complex-deep-learning-models-often-considered-black-boxes",children:"Q5: How can data practitioners ensure transparency and explainability in complex deep learning models, often considered \u201cblack boxes\u201d?"}),"\n",(0,n.jsxs)(i.p,{children:["Ensuring transparency and explainability in complex deep learning models, often regarded as \u201cblack boxes,\u201d is indeed a challenging task. However, it\u2019s not impossible, and there are several strategies that data scientists and engineers can employ. Firstly, they can use ",(0,n.jsx)(i.em,{children:"model-agnostic techniques for interpretability"}),". These methods, such as ",(0,n.jsx)(i.a,{href:"https://github.com/marcotcr/lime",children:"LIME"})," (Local Interpretable Model-agnostic Explanations) or ",(0,n.jsx)(i.a,{href:"https://github.com/shap/shap",children:"SHAP"})," (SHapley Additive exPlanations), can provide insights into the model\u2019s decision-making process without needing to fully understand the complex neural network. Secondly, researchers are actively working on developing more interpretable deep learning architectures. These models are designed with transparency and explainability in mind and can provide clearer insights into the features and patterns the model is using to make decisions. Additionally, creating transparency in the data pipeline is crucial. Documenting data sources, preprocessing steps, and feature engineering can help explain why certain decisions were made based on the input data. Lastly, adopting industry standards and best practices for transparency can guide data scientists and engineers in their efforts to make AI more transparent and explainable, even in complex deep learning scenarios."]}),"\n",(0,n.jsx)(i.h3,{id:"q6-what-challenges-can-arise-when-implementing-transparency-and-explainability-in-ai-systems-and-how-can-they-be-overcome",children:"Q6: What challenges can arise when implementing transparency and explainability in AI systems, and how can they be overcome?"}),"\n",(0,n.jsxs)(i.p,{children:["Implementing transparency and explainability in AI systems can pose several challenges. One common challenge is the ",(0,n.jsx)(i.em,{children:"trade-off between model complexity and interpretability"}),". Deep learning models, for example, can be highly accurate but difficult to interpret. To overcome this, data scientists must strike a balance between model performance and the need for transparency. Additionally, ensuring real-time explainability in applications like autonomous vehicles or medical diagnosis can be challenging, as explanations need to be provided instantly. Addressing this challenge requires the development of efficient and fast-explaining techniques. Furthermore, as AI systems evolve and adapt, maintaining transparency becomes an ongoing challenge. Data scientists need to implement continuous ",(0,n.jsx)(i.em,{children:"monitoring"})," and auditing processes to ensure that models remain transparent and explainable as they are updated. Finally, there may be resistance from organizations or stakeholders who are concerned that transparency could reveal proprietary or sensitive information. Addressing this challenge involves finding ways to provide explanations without divulging confidential data, possibly through the use of ",(0,n.jsx)(i.em,{children:"privacy-preserving techniques"})," or summarized explanations."]}),"\n",(0,n.jsx)(i.h2,{id:"data-privacy-and-security",children:"Data Privacy and Security"}),"\n",(0,n.jsx)(i.p,{children:"Protecting data privacy and ensuring security in AI applications is an ethical obligation that AI practitioners must prioritize. As we leverage vast amounts of personal data to train AI models, the potential for misuse and breaches grows. This topic dives into the critical need to establish robust safeguards and ethical practices to safeguard individuals\u2019 privacy and protect against data breaches."}),"\n",(0,n.jsx)(i.h3,{id:"q7-why-is-data-privacy-and-security-a-significant-concern-in-the-context-of-ai-and-what-are-the-potential-risks-of-neglecting-these-aspects",children:"Q7: Why is data privacy and security a significant concern in the context of AI, and what are the potential risks of neglecting these aspects?"}),"\n",(0,n.jsxs)(i.p,{children:["Data privacy and security are significant concerns in the context of AI because AI systems rely heavily on large and often sensitive datasets. Neglecting these aspects can lead to several potential risks. Firstly, there\u2019s the ",(0,n.jsx)(i.em,{children:"risk of unauthorized access"})," and data breaches, which can result in the exposure of personal information, financial records, or other sensitive data, leading to identity theft, financial fraud, or privacy violations. Secondly, when AI models are trained on data that contains personally identifiable information (",(0,n.jsx)(i.a,{href:"https://en.wikipedia.org/wiki/Personal_data",children:"PII"}),"), there\u2019s a risk of unintentional disclosure of private information during the model\u2019s operation, especially if it\u2019s used in applications like healthcare or finance. Additionally, the misuse of AI technology, if data privacy and security measures are not in place, can result in discriminatory practices, reinforcing existing biases, and harming vulnerable populations. Finally, from a regulatory perspective, non-compliance with data protection laws, such as GDPR or CCPA, can result in significant legal and financial penalties. Therefore, prioritizing data privacy and security in AI is not only an ethical imperative but also a legal and reputational necessity."]}),"\n",(0,n.jsx)(i.h3,{id:"q8-how-can-data-scientists-and-engineers-ensure-robust-data-privacy-and-security-in-ai-projects-especially-when-working-with-sensitive-data",children:"Q8: How can data scientists and engineers ensure robust data privacy and security in AI projects, especially when working with sensitive data?"}),"\n",(0,n.jsxs)(i.p,{children:["Ensuring robust data privacy and security in AI projects, especially when dealing with sensitive data, requires a comprehensive approach. Firstly, data scientists and engineers should implement strong ",(0,n.jsx)(i.em,{children:"data anonymization"})," and ",(0,n.jsx)(i.em,{children:"encryption"})," techniques during data collection, storage, and transmission. This prevents unauthorized access and protects the confidentiality of the data. Secondly, access control mechanisms must be in place to restrict who can access and modify the data and AI models. ",(0,n.jsx)(i.em,{children:"Role-based access control"})," and authentication methods can be effective in this regard. Thirdly, ",(0,n.jsx)(i.em,{children:"data minimization"})," should be a guiding principle. Collect only the data necessary for the specific AI task, reducing the risk associated with excessive data exposure. Additionally, regular security audits and vulnerability assessments should be conducted to identify and address potential weaknesses in the AI system. Collaboration with cybersecurity experts is crucial to stay ahead of evolving threats.\nFurthermore, compliance with data protection regulations should be a priority. Data scientists and engineers should be well-versed in the legal requirements of the regions where their AI systems will operate and ensure that their projects adhere to these standards. Finally, ongoing monitoring and incident response plans should be established to swiftly detect and address any security breaches or privacy violations. By adopting these practices, data scientists and engineers can significantly enhance data privacy and security in AI projects, safeguarding both individuals\u2019 sensitive information and the integrity of AI applications."]}),"\n",(0,n.jsx)(i.h3,{id:"q9-how-can-organizations-balance-data-utilization-for-ai-advancement-with-respecting-individual-data-privacy-rights",children:"Q9: How can organizations balance data utilization for AI advancement with respecting individual data privacy rights?"}),"\n",(0,n.jsxs)(i.p,{children:["Striking a balance between utilizing valuable data for AI advancements and respecting individual data privacy rights is a delicate but essential task. Organizations can begin by implementing ",(0,n.jsx)(i.em,{children:"data anonymization"})," techniques that allow them to use data for AI training and analytics without exposing individuals\u2019 identities. This approach ensures that the data retains its value for insights while minimizing the risks to privacy. Additionally, adopting the principle of ",(0,n.jsx)(i.em,{children:"data minimization"})," is crucial. Collect and store only the data necessary for specific AI tasks, reducing the amount of potentially sensitive information at risk. Moreover, organizations can prioritize transparency by clearly communicating their data usage and privacy policies to users. Providing individuals with informed consent and opt-in choices empowers them to make decisions about how their data is used in AI applications. Lastly, investing in state-of-the-art security measures and compliance with data protection regulations is non-negotiable. This not only safeguards data but also ensures organizations are legally compliant, thereby mitigating risks associated with data breaches and privacy violations. In essence, finding the right balance between data utilization and privacy protection involves a combination of technical, ethical, and legal strategies."]}),"\n",(0,n.jsx)(i.h2,{id:"accountability-and-legal-frameworks",children:"Accountability and Legal Frameworks"}),"\n",(0,n.jsx)(i.p,{children:"Accountability is a central concern in the AI landscape. Determining who is responsible when AI systems fail or cause harm is a complex ethical challenge. This topic explores the development of legal frameworks and ethical standards to hold both individuals and organizations accountable for the consequences of AI technology."}),"\n",(0,n.jsx)(i.h3,{id:"q10-why-is-establishing-accountability-in-ai-crucial-and-how-does-it-relate-to-ethical-considerations",children:"Q10: Why is establishing accountability in AI crucial, and how does it relate to ethical considerations?"}),"\n",(0,n.jsxs)(i.p,{children:["Establishing accountability in AI is crucial because it ensures that individuals and organizations are held ",(0,n.jsx)(i.em,{children:"responsible"})," for the consequences of AI system behavior. This accountability is directly linked to ethical considerations as it promotes transparency, fairness, and the responsible use of AI technology. When accountability is established, it becomes clear who is responsible for designing, developing, and deploying AI systems. This clarity is essential for addressing issues such as bias, discrimination, and harm caused by AI. Ethical considerations demand that those who create and operate AI systems are aware of their ethical obligations and take steps to prevent negative outcomes. Without accountability, it\u2019s challenging to enforce ethical standards and ensure that AI systems serve the best interests of society as a whole."]}),"\n",(0,n.jsx)(i.h3,{id:"q11-how-can-legal-frameworks-address-ai-challenges-and-whats-the-role-of-international-collaboration",children:"Q11: How can legal frameworks address AI challenges, and what\u2019s the role of international collaboration?"}),"\n",(0,n.jsxs)(i.p,{children:["Legal frameworks play a vital role in addressing the complex and evolving challenges posed by AI. These frameworks can provide clear guidelines, standards, and ",(0,n.jsx)(i.em,{children:"enforcement mechanisms"})," for ethical AI development and usage. To effectively address AI-related challenges, legal frameworks should be adaptable and capable of keeping pace with rapidly evolving technology. They should cover various aspects, including data protection, transparency, accountability, and liability. Additionally, international collaborations are essential in this context. AI does not respect national borders, and many AI applications are developed and deployed globally. Collaborative efforts between countries can help harmonize AI regulations, making it easier for organizations to navigate the legal landscape. Organizations like the United Nations and regional bodies such as the European Union have started working on frameworks and guidelines to address AI ethics and governance on a global scale. These collaborations facilitate the sharing of best practices and help establish a unified approach to AI ethics, ultimately fostering a more responsible and accountable AI ecosystem."]}),"\n",(0,n.jsx)(i.h3,{id:"q12-how-can-organizations-and-ai-practitioners-proactively-integrate-accountability-into-their-ai-development-processes",children:"Q12: How can organizations and AI practitioners proactively integrate accountability into their AI development processes?"}),"\n",(0,n.jsxs)(i.p,{children:["Organizations and AI practitioners can proactively integrate accountability into their AI development processes by adopting a holistic approach that encompasses several key steps. First and foremost, they should establish clear lines of responsibility within their teams. This includes designating individuals or teams accountable for AI ethics and compliance, ensuring that ethical considerations are integrated from the project\u2019s inception. Additionally, they should conduct comprehensive ",(0,n.jsx)(i.em,{children:"ethical impact assessments"})," throughout the AI development lifecycle, identifying potential risks and biases and taking steps to mitigate them. This may involve using fairness-aware algorithms, data audits, and bias mitigation techniques."]}),"\n",(0,n.jsxs)(i.p,{children:["Furthermore, organizations can foster a culture of ethical AI by providing training and awareness programs for their employees, promoting ethical decision-making, and incentivizing responsible AI practices. ",(0,n.jsx)(i.em,{children:"Regularly monitoring"})," AI systems post-deployment and collecting user feedback can help identify and rectify ethical concerns as they arise. Lastly, organizations should be prepared to engage with external stakeholders, including regulatory bodies, to demonstrate their commitment to accountability and compliance with legal frameworks. By integrating accountability at every stage of AI development, organizations and practitioners can ensure that ethical considerations are not an ",(0,n.jsx)(i.em,{children:"afterthought"})," but a fundamental part of their AI processes."]}),"\n",(0,n.jsx)(i.h2,{id:"ethical-considerations-in-ai-rd",children:"Ethical Considerations in AI R&D"}),"\n",(0,n.jsx)(i.p,{children:"The research and development of AI technologies come with their own set of ethical considerations. These encompass the choices made during the design and creation of AI systems, from data collection methods to algorithmic decision-making. Examining this topic sheds light on the ethical responsibilities that researchers and engineers bear throughout the AI development lifecycle."}),"\n",(0,n.jsx)(i.h3,{id:"q13-why-must-ethical-considerations-be-part-of-ai-rd-from-the-projects-outset",children:"Q13: Why must ethical considerations be part of AI R&D from the project\u2019s outset?"}),"\n",(0,n.jsxs)(i.p,{children:["It\u2019s crucial for researchers and engineers to consider ethical implications during the early stages of AI R&D because ethical considerations are intertwined with the entire AI development process. Neglecting ethics in the early stages can lead to unintended consequences down the line. Ethical issues, such as bias or privacy concerns, often originate from decisions made in data collection, algorithm design, and model training. By addressing these concerns at the outset, researchers can mitigate potential harm and bias. Additionally, ethical considerations are essential for ",(0,n.jsx)(i.em,{children:"building trust with users"})," and stakeholders. Users are more likely to adopt and trust AI systems that have been developed with ethics in mind. Furthermore, considering ethics from the beginning helps prevent costly rework and legal issues that may arise if ethical concerns are only addressed after an AI system has been deployed. In essence, incorporating ethics into AI R&D from the start is not only responsible but also practical for ensuring the long-term success and impact of AI projects."]}),"\n",(0,n.jsx)(i.h3,{id:"q14-how-can-researchers-handle-ethics-in-ai-development-with-emerging-tech-like-deep-learning",children:"Q14: How can researchers handle ethics in AI development with emerging tech like deep learning?"}),"\n",(0,n.jsxs)(i.p,{children:["Navigating the ethical challenges associated with emerging technologies like deep learning and reinforcement learning requires a proactive and multidimensional approach. Firstly, researchers and engineers should prioritize ",(0,n.jsx)(i.em,{children:"transparency"})," and ",(0,n.jsx)(i.em,{children:"explainability"}),", even in complex models. While deep learning models can be challenging to interpret, efforts should be made to make their decisions understandable. Model-agnostic techniques and interpretability tools can help provide insights into the inner workings of these models. Secondly, they should be aware of the potential for unintended biases to emerge in the data used to train these models. Careful data preprocessing, bias audits, and diverse datasets can help mitigate this risk. Thirdly, involving multidisciplinary teams is essential. Ethicists, social scientists, and domain experts can provide valuable perspectives on the ethical implications of AI technologies and help shape responsible development. Moreover, staying informed about emerging ethical guidelines and standards in the AI field is vital, as these guidelines evolve alongside technology. Finally, considering the long-term impact of AI systems and their potential societal consequences is essential when working with emerging technologies. Researchers and engineers should ask critical questions about the implications of their work and anticipate potential ethical dilemmas, aiming for solutions that prioritize fairness, transparency, and the well-being of society as a whole."]}),"\n",(0,n.jsx)(i.h3,{id:"q15-how-can-researchers-and-engineers-balance-the-pursuit-of-technological-innovation-in-ai-rd-with-ethical-considerations",children:"Q15: How can researchers and engineers balance the pursuit of technological innovation in AI R&D with ethical considerations?"}),"\n",(0,n.jsxs)(i.p,{children:["Balancing technological innovation with ethical considerations in AI R&D requires a mindful approach. Researchers and engineers can begin by setting clear ethical goals and principles at the project\u2019s inception. This involves ",(0,n.jsx)(i.em,{children:"defining ethical boundaries"})," and ensuring that innovation aligns with these principles. Regular ethical reviews and impact assessments can help identify potential conflicts between innovation and ethical considerations. Moreover, interdisciplinary collaboration is invaluable. By involving ethicists, social scientists, and experts from various fields, teams can benefit from diverse perspectives that help steer innovation in ethically responsible directions. Additionally, organizations can establish dedicated ",(0,n.jsx)(i.em,{children:"ethical review boards or committees"})," that evaluate projects from an ethical standpoint, providing guidance and ensuring that innovation aligns with ethical norms. Lastly, transparency is key. Researchers and engineers should communicate their ethical commitments and progress to stakeholders, including the public, to build trust and demonstrate a commitment to responsible innovation. Ultimately, ethical considerations and innovation are not mutually exclusive but should be mutually reinforcing, with ethical boundaries guiding and enhancing the impact of technological advances."]})]})}function h(e={}){const{wrapper:i}={...(0,t.a)(),...e.components};return i?(0,n.jsx)(i,{...e,children:(0,n.jsx)(d,{...e})}):d(e)}},11151:(e,i,a)=>{a.d(i,{Z:()=>o,a:()=>r});var n=a(67294);const t={},s=n.createContext(t);function r(e){const i=n.useContext(s);return n.useMemo((function(){return"function"==typeof e?e(i):{...i,...e}}),[i,e])}function o(e){let i;return i=e.disableParentContext?"function"==typeof e.components?e.components(t):e.components||t:r(e.components),n.createElement(s.Provider,{value:i},e.children)}}}]);
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t=e.languages.javascript,n=/\{(?:[^{}]|\{(?:[^{}]|\{[^{}]*\})*\})+\}/.source,o="(@(?:arg|argument|param|property)\\s+(?:"+n+"\\s+)?)";e.languages.jsdoc=e.languages.extend("javadoclike",{parameter:{pattern:RegExp(o+/(?:(?!\s)[$\w\xA0-\uFFFF.])+(?=\s|$)/.source),lookbehind:!0,inside:{punctuation:/\./}}}),e.languages.insertBefore("jsdoc","keyword",{"optional-parameter":{pattern:RegExp(o+/\[(?:(?!\s)[$\w\xA0-\uFFFF.])+(?:=[^[\]]+)?\](?=\s|$)/.source),lookbehind:!0,inside:{parameter:{pattern:/(^\[)[$\w\xA0-\uFFFF\.]+/,lookbehind:!0,inside:{punctuation:/\./}},code:{pattern:/(=)[\s\S]*(?=\]$)/,lookbehind:!0,inside:t,alias:"language-javascript"},punctuation:/[=[\]]/}},"class-name":[{pattern:RegExp(/(@(?:augments|class|extends|interface|memberof!?|template|this|typedef)\s+(?:<TYPE>\s+)?)[A-Z]\w*(?:\.[A-Z]\w*)*/.source.replace(/<TYPE>/g,(function(){return n}))),lookbehind:!0,inside:{punctuation:/\./}},{pattern:RegExp("(@[a-z]+\\s+)"+n),lookbehind:!0,inside:{string:t.string,number:t.number,boolean:t.boolean,keyword:e.languages.typescript.keyword,operator:/=>|\.\.\.|[&|?:*]/,punctuation:/[.,;=<>{}()[\]]/}}],example:{pattern:/(@example\s+(?!\s))(?:[^@\s]|\s+(?!\s))+?(?=\s*(?:\*\s*)?(?:@\w|\*\/))/,lookbehind:!0,inside:{code:{pattern:/^([\t ]*(?:\*\s*)?)\S.*$/m,lookbehind:!0,inside:t,alias:"language-javascript"}}}}),e.languages.javadoclike.addSupport("javascript",e.languages.jsdoc)}(T),function(e){e.languages.flow=e.languages.extend("javascript",{}),e.languages.insertBefore("flow","keyword",{type:[{pattern:/\b(?:[Bb]oolean|Function|[Nn]umber|[Ss]tring|[Ss]ymbol|any|mixed|null|void)\b/,alias:"class-name"}]}),e.languages.flow["function-variable"].pattern=/(?!\s)[_$a-z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\s*=\s*(?:function\b|(?:\([^()]*\)(?:\s*:\s*\w+)?|(?!\s)[_$a-z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*)\s*=>))/i,delete e.languages.flow.parameter,e.languages.insertBefore("flow","operator",{"flow-punctuation":{pattern:/\{\||\|\}/,alias:"punctuation"}}),Array.isArray(e.languages.flow.keyword)||(e.languages.flow.keyword=[e.languages.flow.keyword]),e.languages.flow.keyword.unshift({pattern:/(^|[^$]\b)(?:Class|declare|opaque|type)\b(?!\$)/,lookbehind:!0},{pattern:/(^|[^$]\B)\$(?:Diff|Enum|Exact|Keys|ObjMap|PropertyType|Record|Shape|Subtype|Supertype|await)\b(?!\$)/,lookbehind:!0})}(T),T.languages.n4js=T.languages.extend("javascript",{keyword:/\b(?:Array|any|boolean|break|case|catch|class|const|constructor|continue|debugger|declare|default|delete|do|else|enum|export|extends|false|finally|for|from|function|get|if|implements|import|in|instanceof|interface|let|module|new|null|number|package|private|protected|public|return|set|static|string|super|switch|this|throw|true|try|typeof|var|void|while|with|yield)\b/}),T.languages.insertBefore("n4js","constant",{annotation:{pattern:/@+\w+/,alias:"operator"}}),T.languages.n4jsd=T.languages.n4js,function(e){function t(e,t){return RegExp(e.replace(/<ID>/g,(function(){return/(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*/.source})),t)}e.languages.insertBefore("javascript","function-variable",{"method-variable":{pattern:RegExp("(\\.\\s*)"+e.languages.javascript["function-variable"].pattern.source),lookbehind:!0,alias:["function-variable","method","function","property-access"]}}),e.languages.insertBefore("javascript","function",{method:{pattern:RegExp("(\\.\\s*)"+e.languages.javascript.function.source),lookbehind:!0,alias:["function","property-access"]}}),e.languages.insertBefore("javascript","constant",{"known-class-name":[{pattern:/\b(?:(?:Float(?:32|64)|(?:Int|Uint)(?:8|16|32)|Uint8Clamped)?Array|ArrayBuffer|BigInt|Boolean|DataView|Date|Error|Function|Intl|JSON|(?:Weak)?(?:Map|Set)|Math|Number|Object|Promise|Proxy|Reflect|RegExp|String|Symbol|WebAssembly)\b/,alias:"class-name"},{pattern:/\b(?:[A-Z]\w*)Error\b/,alias:"class-name"}]}),e.languages.insertBefore("javascript","keyword",{imports:{pattern:t(/(\bimport\b\s*)(?:<ID>(?:\s*,\s*(?:\*\s*as\s+<ID>|\{[^{}]*\}))?|\*\s*as\s+<ID>|\{[^{}]*\})(?=\s*\bfrom\b)/.source),lookbehind:!0,inside:e.languages.javascript},exports:{pattern:t(/(\bexport\b\s*)(?:\*(?:\s*as\s+<ID>)?(?=\s*\bfrom\b)|\{[^{}]*\})/.source),lookbehind:!0,inside:e.languages.javascript}}),e.languages.javascript.keyword.unshift({pattern:/\b(?:as|default|export|from|import)\b/,alias:"module"},{pattern:/\b(?:await|break|catch|continue|do|else|finally|for|if|return|switch|throw|try|while|yield)\b/,alias:"control-flow"},{pattern:/\bnull\b/,alias:["null","nil"]},{pattern:/\bundefined\b/,alias:"nil"}),e.languages.insertBefore("javascript","operator",{spread:{pattern:/\.{3}/,alias:"operator"},arrow:{pattern:/=>/,alias:"operator"}}),e.languages.insertBefore("javascript","punctuation",{"property-access":{pattern:t(/(\.\s*)#?<ID>/.source),lookbehind:!0},"maybe-class-name":{pattern:/(^|[^$\w\xA0-\uFFFF])[A-Z][$\w\xA0-\uFFFF]+/,lookbehind:!0},dom:{pattern:/\b(?:document|(?:local|session)Storage|location|navigator|performance|window)\b/,alias:"variable"},console:{pattern:/\bconsole(?=\s*\.)/,alias:"class-name"}});for(var n=["function","function-variable","method","method-variable","property-access"],o=0;o<n.length;o++){var r=n[o],a=e.languages.javascript[r];r=(a="RegExp"===e.util.type(a)?e.languages.javascript[r]={pattern:a}:a).inside||{};(a.inside=r)["maybe-class-name"]=/^[A-Z][\s\S]*/}}(T),function(e){var t=e.util.clone(e.languages.javascript),n=/(?:\s|\/\/.*(?!.)|\/\*(?:[^*]|\*(?!\/))\*\/)/.source,o=/(?:\{(?:\{(?:\{[^{}]*\}|[^{}])*\}|[^{}])*\})/.source,r=/(?:\{<S>*\.{3}(?:[^{}]|<BRACES>)*\})/.source;function a(e,t){return e=e.replace(/<S>/g,(function(){return n})).replace(/<BRACES>/g,(function(){return o})).replace(/<SPREAD>/g,(function(){return r})),RegExp(e,t)}function i(t){for(var n=[],o=0;o<t.length;o++){var r=t[o],a=!1;"string"!=typeof 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e.languages.kotlin["class-name"];var t={"interpolation-punctuation":{pattern:/^\$\{?|\}$/,alias:"punctuation"},expression:{pattern:/[\s\S]+/,inside:e.languages.kotlin}};e.languages.insertBefore("kotlin","string",{"string-literal":[{pattern:/"""(?:[^$]|\$(?:(?!\{)|\{[^{}]*\}))*?"""/,alias:"multiline",inside:{interpolation:{pattern:/\$(?:[a-z_]\w*|\{[^{}]*\})/i,inside:t},string:/[\s\S]+/}},{pattern:/"(?:[^"\\\r\n$]|\\.|\$(?:(?!\{)|\{[^{}]*\}))*"/,alias:"singleline",inside:{interpolation:{pattern:/((?:^|[^\\])(?:\\{2})*)\$(?:[a-z_]\w*|\{[^{}]*\})/i,lookbehind:!0,inside:t},string:/[\s\S]+/}}],char:{pattern:/'(?:[^'\\\r\n]|\\(?:.|u[a-fA-F0-9]{0,4}))'/,greedy:!0}}),delete e.languages.kotlin.string,e.languages.insertBefore("kotlin","keyword",{annotation:{pattern:/\B@(?:\w+:)?(?:[A-Z]\w*|\[[^\]]+\])/,alias:"builtin"}}),e.languages.insertBefore("kotlin","function",{label:{pattern:/\b\w+@|@\w+\b/,alias:"symbol"}}),e.languages.kt=e.languages.kotlin,e.languages.kts=e.languages.kotlin}(T),T.languages.c=T.languages.extend("clike",{comment:{pattern:/\/\/(?:[^\r\n\\]|\\(?:\r\n?|\n|(?![\r\n])))*|\/\*[\s\S]*?(?:\*\/|$)/,greedy:!0},string:{pattern:/"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},"class-name":{pattern:/(\b(?:enum|struct)\s+(?:__attribute__\s*\(\([\s\S]*?\)\)\s*)?)\w+|\b[a-z]\w*_t\b/,lookbehind:!0},keyword:/\b(?:_Alignas|_Alignof|_Atomic|_Bool|_Complex|_Generic|_Imaginary|_Noreturn|_Static_assert|_Thread_local|__attribute__|asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|inline|int|long|register|return|short|signed|sizeof|static|struct|switch|typedef|typeof|union|unsigned|void|volatile|while)\b/,function:/\b[a-z_]\w*(?=\s*\()/i,number:/(?:\b0x(?:[\da-f]+(?:\.[\da-f]*)?|\.[\da-f]+)(?:p[+-]?\d+)?|(?:\b\d+(?:\.\d*)?|\B\.\d+)(?:e[+-]?\d+)?)[ful]{0,4}/i,operator:/>>=?|<<=?|->|([-+&|:])\1|[?:~]|[-+*/%&|^!=<>]=?/}),T.languages.insertBefore("c","string",{char:{pattern:/'(?:\\(?:\r\n|[\s\S])|[^'\\\r\n]){0,32}'/,greedy:!0}}),T.languages.insertBefore("c","string",{macro:{pattern:/(^[\t 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T.languages.c.boolean,T.languages.objectivec=T.languages.extend("c",{string:{pattern:/@?"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},keyword:/\b(?:asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|in|inline|int|long|register|return|self|short|signed|sizeof|static|struct|super|switch|typedef|typeof|union|unsigned|void|volatile|while)\b|(?:@interface|@end|@implementation|@protocol|@class|@public|@protected|@private|@property|@try|@catch|@finally|@throw|@synthesize|@dynamic|@selector)\b/,operator:/-[->]?|\+\+?|!=?|<<?=?|>>?=?|==?|&&?|\|\|?|[~^%?*\/@]/}),delete 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t=e.languages.javascript,n=/\{(?:[^{}]|\{(?:[^{}]|\{[^{}]*\})*\})+\}/.source,o="(@(?:arg|argument|param|property)\\s+(?:"+n+"\\s+)?)";e.languages.jsdoc=e.languages.extend("javadoclike",{parameter:{pattern:RegExp(o+/(?:(?!\s)[$\w\xA0-\uFFFF.])+(?=\s|$)/.source),lookbehind:!0,inside:{punctuation:/\./}}}),e.languages.insertBefore("jsdoc","keyword",{"optional-parameter":{pattern:RegExp(o+/\[(?:(?!\s)[$\w\xA0-\uFFFF.])+(?:=[^[\]]+)?\](?=\s|$)/.source),lookbehind:!0,inside:{parameter:{pattern:/(^\[)[$\w\xA0-\uFFFF\.]+/,lookbehind:!0,inside:{punctuation:/\./}},code:{pattern:/(=)[\s\S]*(?=\]$)/,lookbehind:!0,inside:t,alias:"language-javascript"},punctuation:/[=[\]]/}},"class-name":[{pattern:RegExp(/(@(?:augments|class|extends|interface|memberof!?|template|this|typedef)\s+(?:<TYPE>\s+)?)[A-Z]\w*(?:\.[A-Z]\w*)*/.source.replace(/<TYPE>/g,(function(){return n}))),lookbehind:!0,inside:{punctuation:/\./}},{pattern:RegExp("(@[a-z]+\\s+)"+n),lookbehind:!0,inside:{string:t.string,number:t.number,boolean:t.boolean,keyword:e.languages.typescript.keyword,operator:/=>|\.\.\.|[&|?:*]/,punctuation:/[.,;=<>{}()[\]]/}}],example:{pattern:/(@example\s+(?!\s))(?:[^@\s]|\s+(?!\s))+?(?=\s*(?:\*\s*)?(?:@\w|\*\/))/,lookbehind:!0,inside:{code:{pattern:/^([\t ]*(?:\*\s*)?)\S.*$/m,lookbehind:!0,inside:t,alias:"language-javascript"}}}}),e.languages.javadoclike.addSupport("javascript",e.languages.jsdoc)}(T),function(e){e.languages.flow=e.languages.extend("javascript",{}),e.languages.insertBefore("flow","keyword",{type:[{pattern:/\b(?:[Bb]oolean|Function|[Nn]umber|[Ss]tring|[Ss]ymbol|any|mixed|null|void)\b/,alias:"class-name"}]}),e.languages.flow["function-variable"].pattern=/(?!\s)[_$a-z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\s*=\s*(?:function\b|(?:\([^()]*\)(?:\s*:\s*\w+)?|(?!\s)[_$a-z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*)\s*=>))/i,delete e.languages.flow.parameter,e.languages.insertBefore("flow","operator",{"flow-punctuation":{pattern:/\{\||\|\}/,alias:"punctuation"}}),Array.isArray(e.languages.flow.keyword)||(e.languages.flow.keyword=[e.languages.flow.keyword]),e.languages.flow.keyword.unshift({pattern:/(^|[^$]\b)(?:Class|declare|opaque|type)\b(?!\$)/,lookbehind:!0},{pattern:/(^|[^$]\B)\$(?:Diff|Enum|Exact|Keys|ObjMap|PropertyType|Record|Shape|Subtype|Supertype|await)\b(?!\$)/,lookbehind:!0})}(T),T.languages.n4js=T.languages.extend("javascript",{keyword:/\b(?:Array|any|boolean|break|case|catch|class|const|constructor|continue|debugger|declare|default|delete|do|else|enum|export|extends|false|finally|for|from|function|get|if|implements|import|in|instanceof|interface|let|module|new|null|number|package|private|protected|public|return|set|static|string|super|switch|this|throw|true|try|typeof|var|void|while|with|yield)\b/}),T.languages.insertBefore("n4js","constant",{annotation:{pattern:/@+\w+/,alias:"operator"}}),T.languages.n4jsd=T.languages.n4js,function(e){function t(e,t){return RegExp(e.replace(/<ID>/g,(function(){return/(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*/.source})),t)}e.languages.insertBefore("javascript","function-variable",{"method-variable":{pattern:RegExp("(\\.\\s*)"+e.languages.javascript["function-variable"].pattern.source),lookbehind:!0,alias:["function-variable","method","function","property-access"]}}),e.languages.insertBefore("javascript","function",{method:{pattern:RegExp("(\\.\\s*)"+e.languages.javascript.function.source),lookbehind:!0,alias:["function","property-access"]}}),e.languages.insertBefore("javascript","constant",{"known-class-name":[{pattern:/\b(?:(?:Float(?:32|64)|(?:Int|Uint)(?:8|16|32)|Uint8Clamped)?Array|ArrayBuffer|BigInt|Boolean|DataView|Date|Error|Function|Intl|JSON|(?:Weak)?(?:Map|Set)|Math|Number|Object|Promise|Proxy|Reflect|RegExp|String|Symbol|WebAssembly)\b/,alias:"class-name"},{pattern:/\b(?:[A-Z]\w*)Error\b/,alias:"class-name"}]}),e.languages.insertBefore("javascript","keyword",{imports:{pattern:t(/(\bimport\b\s*)(?:<ID>(?:\s*,\s*(?:\*\s*as\s+<ID>|\{[^{}]*\}))?|\*\s*as\s+<ID>|\{[^{}]*\})(?=\s*\bfrom\b)/.source),lookbehind:!0,inside:e.languages.javascript},exports:{pattern:t(/(\bexport\b\s*)(?:\*(?:\s*as\s+<ID>)?(?=\s*\bfrom\b)|\{[^{}]*\})/.source),lookbehind:!0,inside:e.languages.javascript}}),e.languages.javascript.keyword.unshift({pattern:/\b(?:as|default|export|from|import)\b/,alias:"module"},{pattern:/\b(?:await|break|catch|continue|do|else|finally|for|if|return|switch|throw|try|while|yield)\b/,alias:"control-flow"},{pattern:/\bnull\b/,alias:["null","nil"]},{pattern:/\bundefined\b/,alias:"nil"}),e.languages.insertBefore("javascript","operator",{spread:{pattern:/\.{3}/,alias:"operator"},arrow:{pattern:/=>/,alias:"operator"}}),e.languages.insertBefore("javascript","punctuation",{"property-access":{pattern:t(/(\.\s*)#?<ID>/.source),lookbehind:!0},"maybe-class-name":{pattern:/(^|[^$\w\xA0-\uFFFF])[A-Z][$\w\xA0-\uFFFF]+/,lookbehind:!0},dom:{pattern:/\b(?:document|(?:local|session)Storage|location|navigator|performance|window)\b/,alias:"variable"},console:{pattern:/\bconsole(?=\s*\.)/,alias:"class-name"}});for(var n=["function","function-variable","method","method-variable","property-access"],o=0;o<n.length;o++){var r=n[o],a=e.languages.javascript[r];r=(a="RegExp"===e.util.type(a)?e.languages.javascript[r]={pattern:a}:a).inside||{};(a.inside=r)["maybe-class-name"]=/^[A-Z][\s\S]*/}}(T),function(e){var t=e.util.clone(e.languages.javascript),n=/(?:\s|\/\/.*(?!.)|\/\*(?:[^*]|\*(?!\/))\*\/)/.source,o=/(?:\{(?:\{(?:\{[^{}]*\}|[^{}])*\}|[^{}])*\})/.source,r=/(?:\{<S>*\.{3}(?:[^{}]|<BRACES>)*\})/.source;function a(e,t){return e=e.replace(/<S>/g,(function(){return n})).replace(/<BRACES>/g,(function(){return o})).replace(/<SPREAD>/g,(function(){return r})),RegExp(e,t)}function i(t){for(var n=[],o=0;o<t.length;o++){var r=t[o],a=!1;"string"!=typeof 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e.languages.kotlin["class-name"];var t={"interpolation-punctuation":{pattern:/^\$\{?|\}$/,alias:"punctuation"},expression:{pattern:/[\s\S]+/,inside:e.languages.kotlin}};e.languages.insertBefore("kotlin","string",{"string-literal":[{pattern:/"""(?:[^$]|\$(?:(?!\{)|\{[^{}]*\}))*?"""/,alias:"multiline",inside:{interpolation:{pattern:/\$(?:[a-z_]\w*|\{[^{}]*\})/i,inside:t},string:/[\s\S]+/}},{pattern:/"(?:[^"\\\r\n$]|\\.|\$(?:(?!\{)|\{[^{}]*\}))*"/,alias:"singleline",inside:{interpolation:{pattern:/((?:^|[^\\])(?:\\{2})*)\$(?:[a-z_]\w*|\{[^{}]*\})/i,lookbehind:!0,inside:t},string:/[\s\S]+/}}],char:{pattern:/'(?:[^'\\\r\n]|\\(?:.|u[a-fA-F0-9]{0,4}))'/,greedy:!0}}),delete e.languages.kotlin.string,e.languages.insertBefore("kotlin","keyword",{annotation:{pattern:/\B@(?:\w+:)?(?:[A-Z]\w*|\[[^\]]+\])/,alias:"builtin"}}),e.languages.insertBefore("kotlin","function",{label:{pattern:/\b\w+@|@\w+\b/,alias:"symbol"}}),e.languages.kt=e.languages.kotlin,e.languages.kts=e.languages.kotlin}(T),T.languages.c=T.languages.extend("clike",{comment:{pattern:/\/\/(?:[^\r\n\\]|\\(?:\r\n?|\n|(?![\r\n])))*|\/\*[\s\S]*?(?:\*\/|$)/,greedy:!0},string:{pattern:/"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},"class-name":{pattern:/(\b(?:enum|struct)\s+(?:__attribute__\s*\(\([\s\S]*?\)\)\s*)?)\w+|\b[a-z]\w*_t\b/,lookbehind:!0},keyword:/\b(?:_Alignas|_Alignof|_Atomic|_Bool|_Complex|_Generic|_Imaginary|_Noreturn|_Static_assert|_Thread_local|__attribute__|asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|inline|int|long|register|return|short|signed|sizeof|static|struct|switch|typedef|typeof|union|unsigned|void|volatile|while)\b/,function:/\b[a-z_]\w*(?=\s*\()/i,number:/(?:\b0x(?:[\da-f]+(?:\.[\da-f]*)?|\.[\da-f]+)(?:p[+-]?\d+)?|(?:\b\d+(?:\.\d*)?|\B\.\d+)(?:e[+-]?\d+)?)[ful]{0,4}/i,operator:/>>=?|<<=?|->|([-+&|:])\1|[?:~]|[-+*/%&|^!=<>]=?/}),T.languages.insertBefore("c","string",{char:{pattern:/'(?:\\(?:\r\n|[\s\S])|[^'\\\r\n]){0,32}'/,greedy:!0}}),T.languages.insertBefore("c","string",{macro:{pattern:/(^[\t 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T.languages.c.boolean,T.languages.objectivec=T.languages.extend("c",{string:{pattern:/@?"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},keyword:/\b(?:asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|in|inline|int|long|register|return|self|short|signed|sizeof|static|struct|super|switch|typedef|typeof|union|unsigned|void|volatile|while)\b|(?:@interface|@end|@implementation|@protocol|@class|@public|@protected|@private|@property|@try|@catch|@finally|@throw|@synthesize|@dynamic|@selector)\b/,operator:/-[->]?|\+\+?|!=?|<<?=?|>>?=?|==?|&&?|\|\|?|[~^%?*\/@]/}),delete 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-->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data Privacy">​</a></h2>
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-->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data Privacy">​</a></h2>
 <p>In the digital era, data is the new oil. It fuels innovation, drives decision-making, and propels businesses towards success. But with great power comes great responsibility. As data scientists, we must ensure that the data we handle is not misused or exploited, causing harm to the individuals it represents. Enter the twin superheroes of data protection: Anonymization and Pseudonymization.</p>
 <p>Anonymization and Pseudonymization are two essential techniques in the realm of data privacy. They serve as the yin and yang, balancing the need for data utility with the imperative of privacy protection. While they may seem similar at first glance, each has its distinct characteristics and use cases.</p>
 <p><strong>Anonymization</strong> is the process of <em>irreversibly</em> transforming data in such a way that a data subject can no longer be identified directly or indirectly. It’s like a one-way ticket: once the data is anonymized, there’s no going back. It’s a powerful tool for protecting privacy, but it also means that certain analyses requiring linkage back to the original data are off the table.</p>
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Pitfalls</a></li><li><a href="/portfolio/blog/parquet">April 9, 2024<!-- --> - <!-- -->Why is Parquet format so popular?</a></li><li><a href="/portfolio/blog/keras-core">April 9, 2024<!-- --> - <!-- -->Introducing Keras Core</a></li><li><a href="/portfolio/blog/guideline-ml">April 9, 2024<!-- --> - <!-- -->Advanced Guidelines for ML Model Training</a></li><li><a href="/portfolio/blog/get-rich-quick">April 9, 2024<!-- --> - <!-- -->Get-Rich-Quick Schemes are a Bad Idea</a></li><li><a href="/portfolio/blog/ethics">April 9, 2024<!-- --> - <!-- -->Exploring AI Ethics: 15 Key Q&amp;As</a></li><li><a href="/portfolio/blog/anonym-pseudonym">April 9, 2024<!-- --> - <!-- -->Anonymization vs. Pseudonymization</a></li></ul></div></div></div></section></main></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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diff --git a/blog/atom.xml b/blog/atom.xml
index 0ba17805..c482ae70 100644
--- a/blog/atom.xml
+++ b/blog/atom.xml
@@ -2,7 +2,7 @@
 <feed xmlns="http://www.w3.org/2005/Atom">
     <id>https://smortezah.github.io/portfolio/blog</id>
     <title>Portfolio Blog</title>
-    <updated>2024-04-05T14:49:00.000Z</updated>
+    <updated>2024-04-09T10:32:08.000Z</updated>
     <generator>https://github.com/jpmonette/feed</generator>
     <link rel="alternate" href="https://smortezah.github.io/portfolio/blog"/>
     <subtitle>Portfolio Blog</subtitle>
@@ -11,7 +11,7 @@
         <title type="html"><![CDATA[Anonymization vs. Pseudonymization]]></title>
         <id>https://smortezah.github.io/portfolio/blog/anonym-pseudonym</id>
         <link href="https://smortezah.github.io/portfolio/blog/anonym-pseudonym"/>
-        <updated>2024-04-05T14:49:00.000Z</updated>
+        <updated>2024-04-09T10:32:08.000Z</updated>
         <summary type="html"><![CDATA[The Yin and Yang of Data Privacy]]></summary>
         <content type="html"><![CDATA[<h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="https://smortezah.github.io/portfolio/blog/anonym-pseudonym#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data Privacy">​</a></h2>
 <p>In the digital era, data is the new oil. It fuels innovation, drives decision-making, and propels businesses towards success. But with great power comes great responsibility. As data scientists, we must ensure that the data we handle is not misused or exploited, causing harm to the individuals it represents. Enter the twin superheroes of data protection: Anonymization and Pseudonymization.</p>
@@ -81,7 +81,7 @@
         <title type="html"><![CDATA[Exploring AI Ethics: 15 Key Q&As]]></title>
         <id>https://smortezah.github.io/portfolio/blog/ethics</id>
         <link href="https://smortezah.github.io/portfolio/blog/ethics"/>
-        <updated>2024-04-05T14:49:00.000Z</updated>
+        <updated>2024-04-09T10:32:08.000Z</updated>
         <summary type="html"><![CDATA[Bias and Fairness in AI]]></summary>
         <content type="html"><![CDATA[<h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="https://smortezah.github.io/portfolio/blog/ethics#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
 <p>In the realm of artificial intelligence, addressing the issue of bias and ensuring fairness in algorithms is paramount. AI systems have the potential to perpetuate and even amplify societal biases if not carefully designed and monitored. Exploring this topic delves into the ethical responsibility of AI researchers and practitioners to mitigate biases and promote equity in AI applications.</p>
@@ -140,7 +140,7 @@ Furthermore, compliance with data protection regulations should be a priority. D
         <title type="html"><![CDATA[Get-Rich-Quick Schemes are a Bad Idea]]></title>
         <id>https://smortezah.github.io/portfolio/blog/get-rich-quick</id>
         <link href="https://smortezah.github.io/portfolio/blog/get-rich-quick"/>
-        <updated>2024-04-05T14:49:00.000Z</updated>
+        <updated>2024-04-09T10:32:08.000Z</updated>
         <summary type="html"><![CDATA[Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.]]></summary>
         <content type="html"><![CDATA[<p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
 <p>Let’s face it, we all want to be successful and financially stable. And with the booming field of data science and machine learning, it’s tempting to believe that we can achieve our financial dreams by simply learning a few skills and jumping on the bandwagon. Unfortunately, the reality is far from that.</p>
@@ -189,7 +189,7 @@ Furthermore, compliance with data protection regulations should be a priority. D
         <title type="html"><![CDATA[Advanced Guidelines for ML Model Training]]></title>
         <id>https://smortezah.github.io/portfolio/blog/guideline-ml</id>
         <link href="https://smortezah.github.io/portfolio/blog/guideline-ml"/>
-        <updated>2024-04-05T14:49:00.000Z</updated>
+        <updated>2024-04-09T10:32:08.000Z</updated>
         <summary type="html"><![CDATA[Understanding Model Training]]></summary>
         <content type="html"><![CDATA[<h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="https://smortezah.github.io/portfolio/blog/guideline-ml#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
 <p>Welcome to the captivating realm of machine learning, where algorithms breathe life into data and unveil patterns that were once hidden in the shadows. Before we dive into the intricate dance of code and data, let’s take a moment to understand the essence of model training.</p>
@@ -307,7 +307,7 @@ Furthermore, compliance with data protection regulations should be a priority. D
         <title type="html"><![CDATA[Introducing Keras Core]]></title>
         <id>https://smortezah.github.io/portfolio/blog/keras-core</id>
         <link href="https://smortezah.github.io/portfolio/blog/keras-core"/>
-        <updated>2024-04-05T14:49:00.000Z</updated>
+        <updated>2024-04-09T10:32:08.000Z</updated>
         <summary type="html"><![CDATA[Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.]]></summary>
         <content type="html"><![CDATA[<p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
 <p>Exciting times lie ahead!</p>
@@ -341,7 +341,7 @@ Furthermore, compliance with data protection regulations should be a priority. D
         <title type="html"><![CDATA[Why is Parquet format so popular?]]></title>
         <id>https://smortezah.github.io/portfolio/blog/parquet</id>
         <link href="https://smortezah.github.io/portfolio/blog/parquet"/>
-        <updated>2024-04-05T14:49:00.000Z</updated>
+        <updated>2024-04-09T10:32:08.000Z</updated>
         <summary type="html"><![CDATA[Introduction]]></summary>
         <content type="html"><![CDATA[<h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="https://smortezah.github.io/portfolio/blog/parquet#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
 <p><a href="https://parquet.apache.org/" target="_blank" rel="noopener noreferrer">Parquet</a> is a popular columnar storage format for big data processing. It’s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we’ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we’ll compare it to the popular pandas DataFrame.</p>
@@ -375,7 +375,7 @@ Furthermore, compliance with data protection regulations should be a priority. D
         <title type="html"><![CDATA[Venn Diagrams: Art & Pitfalls]]></title>
         <id>https://smortezah.github.io/portfolio/blog/venn</id>
         <link href="https://smortezah.github.io/portfolio/blog/venn"/>
-        <updated>2024-04-05T14:49:00.000Z</updated>
+        <updated>2024-04-09T10:32:08.000Z</updated>
         <summary type="html"><![CDATA[In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.]]></summary>
         <content type="html"><![CDATA[<p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p>
 <p>The followings are some reasons why Venn diagrams can sometimes be considered misleading.</p>
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read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
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sidebarItemLinkActive_I1ZP" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><article itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Bias and Fairness in AI"><header><h1 class="title_f1Hy" itemprop="headline">Exploring AI Ethics: 15 Key Q&amp;As</h1><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->16 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
 <p>In the realm of artificial intelligence, addressing the issue of bias and ensuring fairness in algorithms is paramount. AI systems have the potential to perpetuate and even amplify societal biases if not carefully designed and monitored. Exploring this topic delves into the ethical responsibility of AI researchers and practitioners to mitigate biases and promote equity in AI applications.</p>
 <h3 class="anchor anchorWithStickyNavbar_LWe7" id="q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications">Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?<a href="#q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications" class="hash-link" aria-label="Direct link to Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?" title="Direct link to Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?">​</a></h3>
 <p>Addressing bias and ensuring fairness in AI is of paramount importance in real-world applications because these technologies are increasingly integrated into <em>decision-making</em> processes across various domains, from finance and healthcare to criminal justice. When AI models exhibit bias, they can perpetuate and even exacerbate <em>societal inequalities</em>. For example, biased algorithms in lending can lead to discrimination against marginalized groups, and biased facial recognition systems may lead to wrongful arrests. These issues not only <em>undermine trust in AI</em> but also have real-life consequences for individuals and communities. Thus, addressing bias and ensuring fairness is an ethical imperative, promoting equity, and mitigating the harmful impact of AI on vulnerable populations.</p>
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Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science."><header><h1 class="title_f1Hy" itemprop="headline">Get-Rich-Quick Schemes are a Bad Idea</h1><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
 <p>Let’s face it, we all want to be successful and financially stable. And with the booming field of data science and machine learning, it’s tempting to believe that we can achieve our financial dreams by simply learning a few skills and jumping on the bandwagon. Unfortunately, the reality is far from that.</p>
 <p>Here, we’ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let’s debunk some myths!</p>
 <h2 class="anchor anchorWithStickyNavbar_LWe7" id="myth-1-you-can-become-a-data-science-expert-in-a-few-weeks">Myth #1: You can become a data science expert in a few weeks<a href="#myth-1-you-can-become-a-data-science-expert-in-a-few-weeks" class="hash-link" aria-label="Direct link to Myth #1: You can become a data science expert in a few weeks" title="Direct link to Myth #1: You can become a data science expert in a few weeks">​</a></h2>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a aria-current="page" class="sidebarItemLink_mo7H sidebarItemLinkActive_I1ZP" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><article itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Understanding Model Training"><header><h1 class="title_f1Hy" itemprop="headline">Advanced Guidelines for ML Model Training</h1><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->13 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a aria-current="page" class="sidebarItemLink_mo7H sidebarItemLinkActive_I1ZP" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><article itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Understanding Model Training"><header><h1 class="title_f1Hy" itemprop="headline">Advanced Guidelines for ML Model Training</h1><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->13 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
 <p>Welcome to the captivating realm of machine learning, where algorithms breathe life into data and unveil patterns that were once hidden in the shadows. Before we dive into the intricate dance of code and data, let’s take a moment to understand the essence of model training.</p>
 <p>Imagine yourself as an artisan, crafting a masterpiece from raw materials. Just as a painter starts with a blank canvas, you begin with a dataset rich in information. This dataset is your palette, and your model is the brush that will paint the future. 🎨🤖</p>
 <p>Model training is the process of imbuing your creation with the ability to learn from data and make predictions. Just as a symphony conductor guides each musician to play in harmony, you guide your model through the data.</p>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="The Yin and Yang of Data Privacy"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data Privacy">​</a></h2>
 <p>In the digital era, data is the new oil. It fuels innovation, drives decision-making, and propels businesses towards success. But with great power comes great responsibility. As data scientists, we must ensure that the data we handle is not misused or exploited, causing harm to the individuals it represents. Enter the twin superheroes of data protection: Anonymization and Pseudonymization.</p>
-<p>Anonymization and Pseudonymization are two essential techniques in the realm of data privacy. They serve as the yin and yang, balancing the need for data utility with the imperative of privacy protection. While they may seem similar at first glance, each has its distinct characteristics and use cases.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-privacy">Data Privacy</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/anonymization">Anonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pseudonymization">Pseudonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/python">Python</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Anonymization vs. Pseudonymization" href="/portfolio/blog/anonym-pseudonym"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Bias and Fairness in AI"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->16 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
+<p>Anonymization and Pseudonymization are two essential techniques in the realm of data privacy. They serve as the yin and yang, balancing the need for data utility with the imperative of privacy protection. While they may seem similar at first glance, each has its distinct characteristics and use cases.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-privacy">Data Privacy</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/anonymization">Anonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pseudonymization">Pseudonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/python">Python</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Anonymization vs. Pseudonymization" href="/portfolio/blog/anonym-pseudonym"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Bias and Fairness in AI"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->16 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
 <p>In the realm of artificial intelligence, addressing the issue of bias and ensuring fairness in algorithms is paramount. AI systems have the potential to perpetuate and even amplify societal biases if not carefully designed and monitored. Exploring this topic delves into the ethical responsibility of AI researchers and practitioners to mitigate biases and promote equity in AI applications.</p>
 <h3 class="anchor anchorWithStickyNavbar_LWe7" id="q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications">Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?<a href="#q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications" class="hash-link" aria-label="Direct link to Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?" title="Direct link to Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?">​</a></h3>
-<p>Addressing bias and ensuring fairness in AI is of paramount importance in real-world applications because these technologies are increasingly integrated into <em>decision-making</em> processes across various domains, from finance and healthcare to criminal justice. When AI models exhibit bias, they can perpetuate and even exacerbate <em>societal inequalities</em>. For example, biased algorithms in lending can lead to discrimination against marginalized groups, and biased facial recognition systems may lead to wrongful arrests. These issues not only <em>undermine trust in AI</em> but also have real-life consequences for individuals and communities. Thus, addressing bias and ensuring fairness is an ethical imperative, promoting equity, and mitigating the harmful impact of AI on vulnerable populations.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ethics">Ethics</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/bias">Bias</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/transparency">Transparency</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/privacy">Privacy</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Exploring AI Ethics: 15 Key Q&amp;As" href="/portfolio/blog/ethics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
+<p>Addressing bias and ensuring fairness in AI is of paramount importance in real-world applications because these technologies are increasingly integrated into <em>decision-making</em> processes across various domains, from finance and healthcare to criminal justice. When AI models exhibit bias, they can perpetuate and even exacerbate <em>societal inequalities</em>. For example, biased algorithms in lending can lead to discrimination against marginalized groups, and biased facial recognition systems may lead to wrongful arrests. These issues not only <em>undermine trust in AI</em> but also have real-life consequences for individuals and communities. Thus, addressing bias and ensuring fairness is an ethical imperative, promoting equity, and mitigating the harmful impact of AI on vulnerable populations.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ethics">Ethics</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/bias">Bias</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/transparency">Transparency</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/privacy">Privacy</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Exploring AI Ethics: 15 Key Q&amp;As" href="/portfolio/blog/ethics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
 <p>Let’s face it, we all want to be successful and financially stable. And with the booming field of data science and machine learning, it’s tempting to believe that we can achieve our financial dreams by simply learning a few skills and jumping on the bandwagon. Unfortunately, the reality is far from that.</p>
-<p>Here, we’ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let’s debunk some myths!</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/get-rich-quick">Get Rich Quick</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/mythology">Mythology</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Get-Rich-Quick Schemes are a Bad Idea" href="/portfolio/blog/get-rich-quick"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Understanding Model Training"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->13 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
+<p>Here, we’ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let’s debunk some myths!</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/get-rich-quick">Get Rich Quick</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/mythology">Mythology</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Get-Rich-Quick Schemes are a Bad Idea" href="/portfolio/blog/get-rich-quick"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Understanding Model Training"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->13 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
 <p>Welcome to the captivating realm of machine learning, where algorithms breathe life into data and unveil patterns that were once hidden in the shadows. Before we dive into the intricate dance of code and data, let’s take a moment to understand the essence of model training.</p>
 <p>Imagine yourself as an artisan, crafting a masterpiece from raw materials. Just as a painter starts with a blank canvas, you begin with a dataset rich in information. This dataset is your palette, and your model is the brush that will paint the future. 🎨🤖</p>
-<p>Model training is the process of imbuing your creation with the ability to learn from data and make predictions. Just as a symphony conductor guides each musician to play in harmony, you guide your model through the data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/model-training">Model Training</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/guidelines">Guidelines</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/neural-networks">Neural Networks</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Advanced Guidelines for ML Model Training" href="/portfolio/blog/guideline-ml"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/keras-core">Introducing Keras Core</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
+<p>Model training is the process of imbuing your creation with the ability to learn from data and make predictions. Just as a symphony conductor guides each musician to play in harmony, you guide your model through the data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/model-training">Model Training</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/guidelines">Guidelines</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/neural-networks">Neural Networks</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Advanced Guidelines for ML Model Training" href="/portfolio/blog/guideline-ml"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/keras-core">Introducing Keras Core</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
 <p>Exciting times lie ahead!</p>
 <h2 class="anchor anchorWithStickyNavbar_LWe7" id="why-use-keras-core">Why Use Keras Core?<a href="#why-use-keras-core" class="hash-link" aria-label="Direct link to Why Use Keras Core?" title="Direct link to Why Use Keras Core?">​</a></h2>
 <p>But wait, why are they making Keras multi-backend again? Let’s take a quick trip down memory lane. Not too long ago, Keras had the ability to run on multiple backends like Theano, TensorFlow, CNTK, and even MXNet. However, in 2018, they decided to focus exclusively on TensorFlow as other backends discontinued development. But times have changed! Fast forward to 2023, and we see TensorFlow dominating the production ML space with a market share of 55% to 60%. On the other hand, PyTorch has captured the ML research realm with a market share of 40% to 45%. Meanwhile, JAX, although with a smaller market share, has gained recognition from leading players in generative AI. It’s clear that each framework has its strengths and user base. Keras Core enables the users to leverage the power of all three frameworks simultaneously.</p>
-<p>Say goodbye to framework silos and welcome the new era of multi-framework ML!</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/keras">Keras</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/py-torch">PyTorch</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/jax">Jax</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/deep-learning">Deep Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Introducing Keras Core" href="/portfolio/blog/keras-core"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Introduction"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/parquet">Why is Parquet format so popular?</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
-<p><a href="https://parquet.apache.org/" target="_blank" rel="noopener noreferrer">Parquet</a> is a popular columnar storage format for big data processing. It’s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we’ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we’ll compare it to the popular pandas DataFrame.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/parquet">Parquet</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-storage">Data Storage</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/spark">Spark</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pandas-dataframe">Pandas Dataframe</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Why is Parquet format so popular?" href="/portfolio/blog/parquet"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/venn">Venn Diagrams: Art &amp; Pitfalls</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-visualization">Data Visualization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/venn-diagrams">Venn Diagrams</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pitfalls">Pitfalls</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/misinterpretations">Misinterpretations</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Venn Diagrams: Art &amp; Pitfalls" href="/portfolio/blog/venn"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
+<p>Say goodbye to framework silos and welcome the new era of multi-framework ML!</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/keras">Keras</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/py-torch">PyTorch</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/jax">Jax</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/deep-learning">Deep Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Introducing Keras Core" href="/portfolio/blog/keras-core"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Introduction"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/parquet">Why is Parquet format so popular?</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
+<p><a href="https://parquet.apache.org/" target="_blank" rel="noopener noreferrer">Parquet</a> is a popular columnar storage format for big data processing. It’s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we’ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we’ll compare it to the popular pandas DataFrame.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/parquet">Parquet</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-storage">Data Storage</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/spark">Spark</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pandas-dataframe">Pandas Dataframe</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Why is Parquet format so popular?" href="/portfolio/blog/parquet"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/venn">Venn Diagrams: Art &amp; Pitfalls</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-visualization">Data Visualization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/venn-diagrams">Venn Diagrams</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pitfalls">Pitfalls</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/misinterpretations">Misinterpretations</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Venn Diagrams: Art &amp; Pitfalls" href="/portfolio/blog/venn"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a aria-current="page" class="sidebarItemLink_mo7H sidebarItemLinkActive_I1ZP" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><article itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."><header><h1 class="title_f1Hy" itemprop="headline">Introducing Keras Core</h1><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a aria-current="page" class="sidebarItemLink_mo7H sidebarItemLinkActive_I1ZP" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><article itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."><header><h1 class="title_f1Hy" itemprop="headline">Introducing Keras Core</h1><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
 <p>Exciting times lie ahead!</p>
 <h2 class="anchor anchorWithStickyNavbar_LWe7" id="why-use-keras-core">Why Use Keras Core?<a href="#why-use-keras-core" class="hash-link" aria-label="Direct link to Why Use Keras Core?" title="Direct link to Why Use Keras Core?">​</a></h2>
 <p>But wait, why are they making Keras multi-backend again? Let’s take a quick trip down memory lane. Not too long ago, Keras had the ability to run on multiple backends like Theano, TensorFlow, CNTK, and even MXNet. However, in 2018, they decided to focus exclusively on TensorFlow as other backends discontinued development. But times have changed! Fast forward to 2023, and we see TensorFlow dominating the production ML space with a market share of 55% to 60%. On the other hand, PyTorch has captured the ML research realm with a market share of 40% to 45%. Meanwhile, JAX, although with a smaller market share, has gained recognition from leading players in generative AI. It’s clear that each framework has its strengths and user base. Keras Core enables the users to leverage the power of all three frameworks simultaneously.</p>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><article itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Introduction"><header><h1 class="title_f1Hy" itemprop="headline">Why is Parquet format so popular?</h1><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
+<script>!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return localStorage.getItem("theme")}catch(t){}}();t(null!==e?e:"light")}(),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div role="region" aria-label="Skip to main content"><a class="skipToContent_fXgn" href="#__docusaurus_skipToContent_fallback">Skip to main content</a></div><nav aria-label="Main" class="navbar navbar--fixed-top"><div class="navbar__inner"><div class="navbar__items"><button aria-label="Toggle navigation bar" aria-expanded="false" class="navbar__toggle clean-btn" type="button"><svg width="30" height="30" viewBox="0 0 30 30" 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M7.05,18.36c0.39-0.39,0.39-1.03,0-1.41c-0.39-0.39-1.03-0.39-1.41,0l-1.06,1.06 c-0.39,0.39-0.39,1.03,0,1.41s1.03,0.39,1.41,0L7.05,18.36z"></path></svg><svg viewBox="0 0 24 24" width="24" height="24" class="darkToggleIcon_wfgR"><path fill="currentColor" d="M9.37,5.51C9.19,6.15,9.1,6.82,9.1,7.5c0,4.08,3.32,7.4,7.4,7.4c0.68,0,1.35-0.09,1.99-0.27C17.45,17.19,14.93,19,12,19 c-3.86,0-7-3.14-7-7C5,9.07,6.81,6.55,9.37,5.51z M12,3c-4.97,0-9,4.03-9,9s4.03,9,9,9s9-4.03,9-9c0-0.46-0.04-0.92-0.1-1.36 c-0.98,1.37-2.58,2.26-4.4,2.26c-2.98,0-5.4-2.42-5.4-5.4c0-1.81,0.89-3.42,2.26-4.4C12.92,3.04,12.46,3,12,3L12,3z"></path></svg></button></div><div class="navbarSearchContainer_Bca1"><button type="button" class="DocSearch DocSearch-Button" aria-label="Search"><span class="DocSearch-Button-Container"><svg width="20" height="20" class="DocSearch-Search-Icon" viewBox="0 0 20 20"><path d="M14.386 14.386l4.0877 4.0877-4.0877-4.0877c-2.9418 2.9419-7.7115 2.9419-10.6533 0-2.9419-2.9418-2.9419-7.7115 0-10.6533 2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">Search</span></span><span class="DocSearch-Button-Keys"></span></button></div></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div></nav><div id="__docusaurus_skipToContent_fallback" class="main-wrapper mainWrapper_z2l0"><div class="container margin-vert--lg"><div class="row"><aside class="col col--3"><nav class="sidebar_re4s thin-scrollbar" aria-label="Blog recent posts navigation"><div class="sidebarItemTitle_pO2u margin-bottom--md">Recent posts</div><ul class="sidebarItemList_Yudw clean-list"><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><article itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Introduction"><header><h1 class="title_f1Hy" itemprop="headline">Why is Parquet format so popular?</h1><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
 <p><a href="https://parquet.apache.org/" target="_blank" rel="noopener noreferrer">Parquet</a> is a popular columnar storage format for big data processing. It’s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we’ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we’ll compare it to the popular pandas DataFrame.</p>
 <h2 class="anchor anchorWithStickyNavbar_LWe7" id="history">History<a href="#history" class="hash-link" aria-label="Direct link to History" title="Direct link to History">​</a></h2>
 <p>The Parquet format was created in 2013 by the Apache Software Foundation’s Parquet project, a collaboration between Twitter, Cloudera, and other organizations. The goal of the project was to create a columnar storage format that was optimized for big data processing and could be used with a variety of data processing frameworks such as Hadoop, Impala, and Hive. The project was a response to the growing need for a more efficient way of storing and processing large datasets as data collection and storage was rapidly increasing. Since its release, the Parquet format has become one of the most popular storage formats for big data, widely used in the industry and adopted by many companies.</p>
diff --git a/blog/rss.xml b/blog/rss.xml
index cfca19aa..76b05a42 100644
--- a/blog/rss.xml
+++ b/blog/rss.xml
@@ -4,7 +4,7 @@
         <title>Portfolio Blog</title>
         <link>https://smortezah.github.io/portfolio/blog</link>
         <description>Portfolio Blog</description>
-        <lastBuildDate>Fri, 05 Apr 2024 14:49:00 GMT</lastBuildDate>
+        <lastBuildDate>Tue, 09 Apr 2024 10:32:08 GMT</lastBuildDate>
         <docs>https://validator.w3.org/feed/docs/rss2.html</docs>
         <generator>https://github.com/jpmonette/feed</generator>
         <language>en</language>
@@ -12,7 +12,7 @@
             <title><![CDATA[Anonymization vs. Pseudonymization]]></title>
             <link>https://smortezah.github.io/portfolio/blog/anonym-pseudonym</link>
             <guid>https://smortezah.github.io/portfolio/blog/anonym-pseudonym</guid>
-            <pubDate>Fri, 05 Apr 2024 14:49:00 GMT</pubDate>
+            <pubDate>Tue, 09 Apr 2024 10:32:08 GMT</pubDate>
             <description><![CDATA[The Yin and Yang of Data Privacy]]></description>
             <content:encoded><![CDATA[<h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="https://smortezah.github.io/portfolio/blog/anonym-pseudonym#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data Privacy">​</a></h2>
 <p>In the digital era, data is the new oil. It fuels innovation, drives decision-making, and propels businesses towards success. But with great power comes great responsibility. As data scientists, we must ensure that the data we handle is not misused or exploited, causing harm to the individuals it represents. Enter the twin superheroes of data protection: Anonymization and Pseudonymization.</p>
@@ -79,7 +79,7 @@
             <title><![CDATA[Exploring AI Ethics: 15 Key Q&As]]></title>
             <link>https://smortezah.github.io/portfolio/blog/ethics</link>
             <guid>https://smortezah.github.io/portfolio/blog/ethics</guid>
-            <pubDate>Fri, 05 Apr 2024 14:49:00 GMT</pubDate>
+            <pubDate>Tue, 09 Apr 2024 10:32:08 GMT</pubDate>
             <description><![CDATA[Bias and Fairness in AI]]></description>
             <content:encoded><![CDATA[<h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="https://smortezah.github.io/portfolio/blog/ethics#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
 <p>In the realm of artificial intelligence, addressing the issue of bias and ensuring fairness in algorithms is paramount. AI systems have the potential to perpetuate and even amplify societal biases if not carefully designed and monitored. Exploring this topic delves into the ethical responsibility of AI researchers and practitioners to mitigate biases and promote equity in AI applications.</p>
@@ -135,7 +135,7 @@ Furthermore, compliance with data protection regulations should be a priority. D
             <title><![CDATA[Get-Rich-Quick Schemes are a Bad Idea]]></title>
             <link>https://smortezah.github.io/portfolio/blog/get-rich-quick</link>
             <guid>https://smortezah.github.io/portfolio/blog/get-rich-quick</guid>
-            <pubDate>Fri, 05 Apr 2024 14:49:00 GMT</pubDate>
+            <pubDate>Tue, 09 Apr 2024 10:32:08 GMT</pubDate>
             <description><![CDATA[Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.]]></description>
             <content:encoded><![CDATA[<p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
 <p>Let’s face it, we all want to be successful and financially stable. And with the booming field of data science and machine learning, it’s tempting to believe that we can achieve our financial dreams by simply learning a few skills and jumping on the bandwagon. Unfortunately, the reality is far from that.</p>
@@ -181,7 +181,7 @@ Furthermore, compliance with data protection regulations should be a priority. D
             <title><![CDATA[Advanced Guidelines for ML Model Training]]></title>
             <link>https://smortezah.github.io/portfolio/blog/guideline-ml</link>
             <guid>https://smortezah.github.io/portfolio/blog/guideline-ml</guid>
-            <pubDate>Fri, 05 Apr 2024 14:49:00 GMT</pubDate>
+            <pubDate>Tue, 09 Apr 2024 10:32:08 GMT</pubDate>
             <description><![CDATA[Understanding Model Training]]></description>
             <content:encoded><![CDATA[<h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="https://smortezah.github.io/portfolio/blog/guideline-ml#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
 <p>Welcome to the captivating realm of machine learning, where algorithms breathe life into data and unveil patterns that were once hidden in the shadows. Before we dive into the intricate dance of code and data, let’s take a moment to understand the essence of model training.</p>
@@ -296,7 +296,7 @@ Furthermore, compliance with data protection regulations should be a priority. D
             <title><![CDATA[Introducing Keras Core]]></title>
             <link>https://smortezah.github.io/portfolio/blog/keras-core</link>
             <guid>https://smortezah.github.io/portfolio/blog/keras-core</guid>
-            <pubDate>Fri, 05 Apr 2024 14:49:00 GMT</pubDate>
+            <pubDate>Tue, 09 Apr 2024 10:32:08 GMT</pubDate>
             <description><![CDATA[Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.]]></description>
             <content:encoded><![CDATA[<p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
 <p>Exciting times lie ahead!</p>
@@ -327,7 +327,7 @@ Furthermore, compliance with data protection regulations should be a priority. D
             <title><![CDATA[Why is Parquet format so popular?]]></title>
             <link>https://smortezah.github.io/portfolio/blog/parquet</link>
             <guid>https://smortezah.github.io/portfolio/blog/parquet</guid>
-            <pubDate>Fri, 05 Apr 2024 14:49:00 GMT</pubDate>
+            <pubDate>Tue, 09 Apr 2024 10:32:08 GMT</pubDate>
             <description><![CDATA[Introduction]]></description>
             <content:encoded><![CDATA[<h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="https://smortezah.github.io/portfolio/blog/parquet#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
 <p><a href="https://parquet.apache.org/" target="_blank" rel="noopener noreferrer">Parquet</a> is a popular columnar storage format for big data processing. It’s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we’ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we’ll compare it to the popular pandas DataFrame.</p>
@@ -358,7 +358,7 @@ Furthermore, compliance with data protection regulations should be a priority. D
             <title><![CDATA[Venn Diagrams: Art & Pitfalls]]></title>
             <link>https://smortezah.github.io/portfolio/blog/venn</link>
             <guid>https://smortezah.github.io/portfolio/blog/venn</guid>
-            <pubDate>Fri, 05 Apr 2024 14:49:00 GMT</pubDate>
+            <pubDate>Tue, 09 Apr 2024 10:32:08 GMT</pubDate>
             <description><![CDATA[In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.]]></description>
             <content:encoded><![CDATA[<p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p>
 <p>The followings are some reasons why Venn diagrams can sometimes be considered misleading.</p>
diff --git a/blog/tags/ai/index.html b/blog/tags/ai/index.html
index a32d313d..5595e932 100644
--- a/blog/tags/ai/index.html
+++ b/blog/tags/ai/index.html
@@ -11,14 +11,14 @@
 <link rel="search" type="application/opensearchdescription+xml" title="Portfolio" href="/portfolio/opensearch.xml">
 
 <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" integrity="sha384-odtC+0UGzzFL/6PNoE8rX/SPcQDXBJ+uRepguP4QkPCm2LBxH3FA3y+fKSiJ+AmM" crossorigin="anonymous"><link rel="stylesheet" href="/portfolio/assets/css/styles.5107866c.css">
-<script src="/portfolio/assets/js/runtime~main.4e331487.js" defer="defer"></script>
-<script src="/portfolio/assets/js/main.1485a207.js" defer="defer"></script>
+<script src="/portfolio/assets/js/runtime~main.748ea0f0.js" defer="defer"></script>
+<script src="/portfolio/assets/js/main.0e6fe7b8.js" defer="defer"></script>
 </head>
 <body class="navigation-with-keyboard">
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AI Ethics: 15 Key Q&amp;As</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->16 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>2 posts tagged with &quot;AI&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Bias and Fairness in AI"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->16 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
 <p>In the realm of artificial intelligence, addressing the issue of bias and ensuring fairness in algorithms is paramount. AI systems have the potential to perpetuate and even amplify societal biases if not carefully designed and monitored. Exploring this topic delves into the ethical responsibility of AI researchers and practitioners to mitigate biases and promote equity in AI applications.</p>
 <h3 class="anchor anchorWithStickyNavbar_LWe7" id="q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications">Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?<a href="#q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications" class="hash-link" aria-label="Direct link to Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?" title="Direct link to Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?">​</a></h3>
-<p>Addressing bias and ensuring fairness in AI is of paramount importance in real-world applications because these technologies are increasingly integrated into <em>decision-making</em> processes across various domains, from finance and healthcare to criminal justice. When AI models exhibit bias, they can perpetuate and even exacerbate <em>societal inequalities</em>. For example, biased algorithms in lending can lead to discrimination against marginalized groups, and biased facial recognition systems may lead to wrongful arrests. These issues not only <em>undermine trust in AI</em> but also have real-life consequences for individuals and communities. Thus, addressing bias and ensuring fairness is an ethical imperative, promoting equity, and mitigating the harmful impact of AI on vulnerable populations.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ethics">Ethics</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/bias">Bias</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/transparency">Transparency</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/privacy">Privacy</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Exploring AI Ethics: 15 Key Q&amp;As" href="/portfolio/blog/ethics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
+<p>Addressing bias and ensuring fairness in AI is of paramount importance in real-world applications because these technologies are increasingly integrated into <em>decision-making</em> processes across various domains, from finance and healthcare to criminal justice. When AI models exhibit bias, they can perpetuate and even exacerbate <em>societal inequalities</em>. For example, biased algorithms in lending can lead to discrimination against marginalized groups, and biased facial recognition systems may lead to wrongful arrests. These issues not only <em>undermine trust in AI</em> but also have real-life consequences for individuals and communities. Thus, addressing bias and ensuring fairness is an ethical imperative, promoting equity, and mitigating the harmful impact of AI on vulnerable populations.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ethics">Ethics</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/bias">Bias</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/transparency">Transparency</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/privacy">Privacy</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Exploring AI Ethics: 15 Key Q&amp;As" href="/portfolio/blog/ethics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
 <p>Let’s face it, we all want to be successful and financially stable. And with the booming field of data science and machine learning, it’s tempting to believe that we can achieve our financial dreams by simply learning a few skills and jumping on the bandwagon. Unfortunately, the reality is far from that.</p>
 <p>Here, we’ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let’s debunk some myths!</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/get-rich-quick">Get Rich Quick</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/mythology">Mythology</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Get-Rich-Quick Schemes are a Bad Idea" href="/portfolio/blog/get-rich-quick"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Anonymization&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="The Yin and Yang of Data Privacy"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data 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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Anonymization&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="The Yin and Yang of Data Privacy"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data Privacy">​</a></h2>
 <p>In the digital era, data is the new oil. It fuels innovation, drives decision-making, and propels businesses towards success. But with great power comes great responsibility. As data scientists, we must ensure that the data we handle is not misused or exploited, causing harm to the individuals it represents. Enter the twin superheroes of data protection: Anonymization and Pseudonymization.</p>
 <p>Anonymization and Pseudonymization are two essential techniques in the realm of data privacy. They serve as the yin and yang, balancing the need for data utility with the imperative of privacy protection. While they may seem similar at first glance, each has its distinct characteristics and use cases.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-privacy">Data Privacy</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/anonymization">Anonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pseudonymization">Pseudonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/python">Python</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Anonymization vs. Pseudonymization" href="/portfolio/blog/anonym-pseudonym"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Bias&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Bias and Fairness in AI"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->16 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
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2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">Search</span></span><span class="DocSearch-Button-Keys"></span></button></div></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div></nav><div id="__docusaurus_skipToContent_fallback" class="main-wrapper mainWrapper_z2l0"><div class="container margin-vert--lg"><div class="row"><aside class="col col--3"><nav class="sidebar_re4s thin-scrollbar" aria-label="Blog recent posts navigation"><div class="sidebarItemTitle_pO2u margin-bottom--md">Recent posts</div><ul class="sidebarItemList_Yudw clean-list"><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Bias&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Bias and Fairness in AI"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->16 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
 <p>In the realm of artificial intelligence, addressing the issue of bias and ensuring fairness in algorithms is paramount. AI systems have the potential to perpetuate and even amplify societal biases if not carefully designed and monitored. Exploring this topic delves into the ethical responsibility of AI researchers and practitioners to mitigate biases and promote equity in AI applications.</p>
 <h3 class="anchor anchorWithStickyNavbar_LWe7" id="q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications">Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?<a href="#q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications" class="hash-link" aria-label="Direct link to Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?" title="Direct link to Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?">​</a></h3>
 <p>Addressing bias and ensuring fairness in AI is of paramount importance in real-world applications because these technologies are increasingly integrated into <em>decision-making</em> processes across various domains, from finance and healthcare to criminal justice. When AI models exhibit bias, they can perpetuate and even exacerbate <em>societal inequalities</em>. For example, biased algorithms in lending can lead to discrimination against marginalized groups, and biased facial recognition systems may lead to wrongful arrests. These issues not only <em>undermine trust in AI</em> but also have real-life consequences for individuals and communities. Thus, addressing bias and ensuring fairness is an ethical imperative, promoting equity, and mitigating the harmful impact of AI on vulnerable populations.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ethics">Ethics</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/bias">Bias</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/transparency">Transparency</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/privacy">Privacy</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Exploring AI Ethics: 15 Key Q&amp;As" href="/portfolio/blog/ethics"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data 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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Data Privacy&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="The Yin and Yang of Data Privacy"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data Privacy">​</a></h2>
 <p>In the digital era, data is the new oil. It fuels innovation, drives decision-making, and propels businesses towards success. But with great power comes great responsibility. As data scientists, we must ensure that the data we handle is not misused or exploited, causing harm to the individuals it represents. Enter the twin superheroes of data protection: Anonymization and Pseudonymization.</p>
 <p>Anonymization and Pseudonymization are two essential techniques in the realm of data privacy. They serve as the yin and yang, balancing the need for data utility with the imperative of privacy protection. While they may seem similar at first glance, each has its distinct characteristics and use cases.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-privacy">Data Privacy</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/anonymization">Anonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pseudonymization">Pseudonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/python">Python</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Anonymization vs. Pseudonymization" href="/portfolio/blog/anonym-pseudonym"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data 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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>5 posts tagged with &quot;Data Science&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="The Yin and Yang of Data Privacy"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data Privacy">​</a></h2>
 <p>In the digital era, data is the new oil. It fuels innovation, drives decision-making, and propels businesses towards success. But with great power comes great responsibility. As data scientists, we must ensure that the data we handle is not misused or exploited, causing harm to the individuals it represents. Enter the twin superheroes of data protection: Anonymization and Pseudonymization.</p>
-<p>Anonymization and Pseudonymization are two essential techniques in the realm of data privacy. They serve as the yin and yang, balancing the need for data utility with the imperative of privacy protection. While they may seem similar at first glance, each has its distinct characteristics and use cases.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-privacy">Data Privacy</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/anonymization">Anonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pseudonymization">Pseudonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/python">Python</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Anonymization vs. Pseudonymization" href="/portfolio/blog/anonym-pseudonym"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
+<p>Anonymization and Pseudonymization are two essential techniques in the realm of data privacy. They serve as the yin and yang, balancing the need for data utility with the imperative of privacy protection. While they may seem similar at first glance, each has its distinct characteristics and use cases.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-privacy">Data Privacy</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/anonymization">Anonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pseudonymization">Pseudonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/python">Python</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Anonymization vs. Pseudonymization" href="/portfolio/blog/anonym-pseudonym"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
 <p>Let’s face it, we all want to be successful and financially stable. And with the booming field of data science and machine learning, it’s tempting to believe that we can achieve our financial dreams by simply learning a few skills and jumping on the bandwagon. Unfortunately, the reality is far from that.</p>
-<p>Here, we’ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let’s debunk some myths!</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/get-rich-quick">Get Rich Quick</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/mythology">Mythology</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Get-Rich-Quick Schemes are a Bad Idea" href="/portfolio/blog/get-rich-quick"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Understanding Model Training"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->13 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
+<p>Here, we’ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let’s debunk some myths!</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/get-rich-quick">Get Rich Quick</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/mythology">Mythology</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Get-Rich-Quick Schemes are a Bad Idea" href="/portfolio/blog/get-rich-quick"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Understanding Model Training"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->13 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
 <p>Welcome to the captivating realm of machine learning, where algorithms breathe life into data and unveil patterns that were once hidden in the shadows. Before we dive into the intricate dance of code and data, let’s take a moment to understand the essence of model training.</p>
 <p>Imagine yourself as an artisan, crafting a masterpiece from raw materials. Just as a painter starts with a blank canvas, you begin with a dataset rich in information. This dataset is your palette, and your model is the brush that will paint the future. 🎨🤖</p>
-<p>Model training is the process of imbuing your creation with the ability to learn from data and make predictions. Just as a symphony conductor guides each musician to play in harmony, you guide your model through the data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/model-training">Model Training</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/guidelines">Guidelines</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/neural-networks">Neural Networks</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Advanced Guidelines for ML Model Training" href="/portfolio/blog/guideline-ml"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Introduction"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/parquet">Why is Parquet format so popular?</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
-<p><a href="https://parquet.apache.org/" target="_blank" rel="noopener noreferrer">Parquet</a> is a popular columnar storage format for big data processing. It’s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we’ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we’ll compare it to the popular pandas DataFrame.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/parquet">Parquet</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-storage">Data Storage</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/spark">Spark</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pandas-dataframe">Pandas Dataframe</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Why is Parquet format so popular?" href="/portfolio/blog/parquet"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/venn">Venn Diagrams: Art &amp; Pitfalls</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-visualization">Data Visualization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/venn-diagrams">Venn Diagrams</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pitfalls">Pitfalls</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/misinterpretations">Misinterpretations</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Venn Diagrams: Art &amp; Pitfalls" href="/portfolio/blog/venn"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
+<p>Model training is the process of imbuing your creation with the ability to learn from data and make predictions. Just as a symphony conductor guides each musician to play in harmony, you guide your model through the data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/model-training">Model Training</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/guidelines">Guidelines</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/neural-networks">Neural Networks</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Advanced Guidelines for ML Model Training" href="/portfolio/blog/guideline-ml"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Introduction"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/parquet">Why is Parquet format so popular?</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
+<p><a href="https://parquet.apache.org/" target="_blank" rel="noopener noreferrer">Parquet</a> is a popular columnar storage format for big data processing. It’s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we’ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we’ll compare it to the popular pandas DataFrame.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/parquet">Parquet</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-storage">Data Storage</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/spark">Spark</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pandas-dataframe">Pandas Dataframe</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Why is Parquet format so popular?" href="/portfolio/blog/parquet"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/venn">Venn Diagrams: Art &amp; Pitfalls</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-visualization">Data Visualization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/venn-diagrams">Venn Diagrams</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pitfalls">Pitfalls</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/misinterpretations">Misinterpretations</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Venn Diagrams: Art &amp; Pitfalls" href="/portfolio/blog/venn"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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Parquet format so popular?</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Data Storage&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Introduction"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/parquet">Why is Parquet format so popular?</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
 <p><a href="https://parquet.apache.org/" target="_blank" rel="noopener noreferrer">Parquet</a> is a popular columnar storage format for big data processing. It’s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we’ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we’ll compare it to the popular pandas DataFrame.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/parquet">Parquet</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-storage">Data Storage</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/spark">Spark</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pandas-dataframe">Pandas Dataframe</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Why is Parquet format so popular?" href="/portfolio/blog/parquet"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Data Visualization&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/venn">Venn Diagrams: Art &amp; Pitfalls</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-visualization">Data Visualization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/venn-diagrams">Venn Diagrams</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pitfalls">Pitfalls</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/misinterpretations">Misinterpretations</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Venn Diagrams: Art &amp; Pitfalls" href="/portfolio/blog/venn"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Data Visualization&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/venn">Venn Diagrams: Art &amp; Pitfalls</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-visualization">Data Visualization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/venn-diagrams">Venn Diagrams</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pitfalls">Pitfalls</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/misinterpretations">Misinterpretations</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Venn Diagrams: Art &amp; Pitfalls" href="/portfolio/blog/venn"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Deep Learning&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/keras-core">Introducing Keras Core</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Deep Learning&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/keras-core">Introducing Keras Core</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
 <p>Exciting times lie ahead!</p>
 <h2 class="anchor anchorWithStickyNavbar_LWe7" id="why-use-keras-core">Why Use Keras Core?<a href="#why-use-keras-core" class="hash-link" aria-label="Direct link to Why Use Keras Core?" title="Direct link to Why Use Keras Core?">​</a></h2>
 <p>But wait, why are they making Keras multi-backend again? Let’s take a quick trip down memory lane. Not too long ago, Keras had the ability to run on multiple backends like Theano, TensorFlow, CNTK, and even MXNet. However, in 2018, they decided to focus exclusively on TensorFlow as other backends discontinued development. But times have changed! Fast forward to 2023, and we see TensorFlow dominating the production ML space with a market share of 55% to 60%. On the other hand, PyTorch has captured the ML research realm with a market share of 40% to 45%. Meanwhile, JAX, although with a smaller market share, has gained recognition from leading players in generative AI. It’s clear that each framework has its strengths and user base. Keras Core enables the users to leverage the power of all three frameworks simultaneously.</p>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->16 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Ethics&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Bias and Fairness in AI"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->16 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
 <p>In the realm of artificial intelligence, addressing the issue of bias and ensuring fairness in algorithms is paramount. AI systems have the potential to perpetuate and even amplify societal biases if not carefully designed and monitored. Exploring this topic delves into the ethical responsibility of AI researchers and practitioners to mitigate biases and promote equity in AI applications.</p>
 <h3 class="anchor anchorWithStickyNavbar_LWe7" id="q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications">Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?<a href="#q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications" class="hash-link" aria-label="Direct link to Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?" title="Direct link to Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?">​</a></h3>
 <p>Addressing bias and ensuring fairness in AI is of paramount importance in real-world applications because these technologies are increasingly integrated into <em>decision-making</em> processes across various domains, from finance and healthcare to criminal justice. When AI models exhibit bias, they can perpetuate and even exacerbate <em>societal inequalities</em>. For example, biased algorithms in lending can lead to discrimination against marginalized groups, and biased facial recognition systems may lead to wrongful arrests. These issues not only <em>undermine trust in AI</em> but also have real-life consequences for individuals and communities. Thus, addressing bias and ensuring fairness is an ethical imperative, promoting equity, and mitigating the harmful impact of AI on vulnerable populations.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ethics">Ethics</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/bias">Bias</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/transparency">Transparency</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/privacy">Privacy</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Exploring AI Ethics: 15 Key Q&amp;As" href="/portfolio/blog/ethics"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Get Rich Quick&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
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2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">Search</span></span><span class="DocSearch-Button-Keys"></span></button></div></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div></nav><div id="__docusaurus_skipToContent_fallback" class="main-wrapper mainWrapper_z2l0"><div class="container margin-vert--lg"><div class="row"><aside class="col col--3"><nav class="sidebar_re4s thin-scrollbar" aria-label="Blog recent posts navigation"><div class="sidebarItemTitle_pO2u margin-bottom--md">Recent posts</div><ul class="sidebarItemList_Yudw clean-list"><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Get Rich Quick&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
 <p>Let’s face it, we all want to be successful and financially stable. And with the booming field of data science and machine learning, it’s tempting to believe that we can achieve our financial dreams by simply learning a few skills and jumping on the bandwagon. Unfortunately, the reality is far from that.</p>
 <p>Here, we’ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let’s debunk some myths!</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/get-rich-quick">Get Rich Quick</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/mythology">Mythology</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Get-Rich-Quick Schemes are a Bad Idea" href="/portfolio/blog/get-rich-quick"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->13 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Guidelines&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Understanding Model Training"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->13 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
 <p>Welcome to the captivating realm of machine learning, where algorithms breathe life into data and unveil patterns that were once hidden in the shadows. Before we dive into the intricate dance of code and data, let’s take a moment to understand the essence of model training.</p>
 <p>Imagine yourself as an artisan, crafting a masterpiece from raw materials. Just as a painter starts with a blank canvas, you begin with a dataset rich in information. This dataset is your palette, and your model is the brush that will paint the future. 🎨🤖</p>
 <p>Model training is the process of imbuing your creation with the ability to learn from data and make predictions. Just as a symphony conductor guides each musician to play in harmony, you guide your model through the data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/model-training">Model Training</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/guidelines">Guidelines</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/neural-networks">Neural Networks</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Advanced Guidelines for ML Model Training" href="/portfolio/blog/guideline-ml"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Jax&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/keras-core">Introducing Keras Core</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
+<script>!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return localStorage.getItem("theme")}catch(t){}}();t(null!==e?e:"light")}(),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div role="region" aria-label="Skip to main content"><a class="skipToContent_fXgn" href="#__docusaurus_skipToContent_fallback">Skip to main content</a></div><nav aria-label="Main" class="navbar navbar--fixed-top"><div class="navbar__inner"><div class="navbar__items"><button aria-label="Toggle navigation bar" aria-expanded="false" class="navbar__toggle clean-btn" type="button"><svg width="30" height="30" viewBox="0 0 30 30" 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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Jax&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/keras-core">Introducing Keras Core</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
 <p>Exciting times lie ahead!</p>
 <h2 class="anchor anchorWithStickyNavbar_LWe7" id="why-use-keras-core">Why Use Keras Core?<a href="#why-use-keras-core" class="hash-link" aria-label="Direct link to Why Use Keras Core?" title="Direct link to Why Use Keras Core?">​</a></h2>
 <p>But wait, why are they making Keras multi-backend again? Let’s take a quick trip down memory lane. Not too long ago, Keras had the ability to run on multiple backends like Theano, TensorFlow, CNTK, and even MXNet. However, in 2018, they decided to focus exclusively on TensorFlow as other backends discontinued development. But times have changed! Fast forward to 2023, and we see TensorFlow dominating the production ML space with a market share of 55% to 60%. On the other hand, PyTorch has captured the ML research realm with a market share of 40% to 45%. Meanwhile, JAX, although with a smaller market share, has gained recognition from leading players in generative AI. It’s clear that each framework has its strengths and user base. Keras Core enables the users to leverage the power of all three frameworks simultaneously.</p>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Keras&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/keras-core">Introducing Keras Core</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Keras&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/keras-core">Introducing Keras Core</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
 <p>Exciting times lie ahead!</p>
 <h2 class="anchor anchorWithStickyNavbar_LWe7" id="why-use-keras-core">Why Use Keras Core?<a href="#why-use-keras-core" class="hash-link" aria-label="Direct link to Why Use Keras Core?" title="Direct link to Why Use Keras Core?">​</a></h2>
 <p>But wait, why are they making Keras multi-backend again? Let’s take a quick trip down memory lane. Not too long ago, Keras had the ability to run on multiple backends like Theano, TensorFlow, CNTK, and even MXNet. However, in 2018, they decided to focus exclusively on TensorFlow as other backends discontinued development. But times have changed! Fast forward to 2023, and we see TensorFlow dominating the production ML space with a market share of 55% to 60%. On the other hand, PyTorch has captured the ML research realm with a market share of 40% to 45%. Meanwhile, JAX, although with a smaller market share, has gained recognition from leading players in generative AI. It’s clear that each framework has its strengths and user base. Keras Core enables the users to leverage the power of all three frameworks simultaneously.</p>
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Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>3 posts tagged with &quot;Machine Learning&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
 <p>Let’s face it, we all want to be successful and financially stable. And with the booming field of data science and machine learning, it’s tempting to believe that we can achieve our financial dreams by simply learning a few skills and jumping on the bandwagon. Unfortunately, the reality is far from that.</p>
-<p>Here, we’ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let’s debunk some myths!</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/get-rich-quick">Get Rich Quick</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/mythology">Mythology</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Get-Rich-Quick Schemes are a Bad Idea" href="/portfolio/blog/get-rich-quick"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Understanding Model Training"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->13 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
+<p>Here, we’ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let’s debunk some myths!</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/get-rich-quick">Get Rich Quick</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/mythology">Mythology</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Get-Rich-Quick Schemes are a Bad Idea" href="/portfolio/blog/get-rich-quick"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Understanding Model Training"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->13 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
 <p>Welcome to the captivating realm of machine learning, where algorithms breathe life into data and unveil patterns that were once hidden in the shadows. Before we dive into the intricate dance of code and data, let’s take a moment to understand the essence of model training.</p>
 <p>Imagine yourself as an artisan, crafting a masterpiece from raw materials. Just as a painter starts with a blank canvas, you begin with a dataset rich in information. This dataset is your palette, and your model is the brush that will paint the future. 🎨🤖</p>
-<p>Model training is the process of imbuing your creation with the ability to learn from data and make predictions. Just as a symphony conductor guides each musician to play in harmony, you guide your model through the data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/model-training">Model Training</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/guidelines">Guidelines</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/neural-networks">Neural Networks</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Advanced Guidelines for ML Model Training" href="/portfolio/blog/guideline-ml"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/keras-core">Introducing Keras Core</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
+<p>Model training is the process of imbuing your creation with the ability to learn from data and make predictions. Just as a symphony conductor guides each musician to play in harmony, you guide your model through the data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/model-training">Model Training</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/guidelines">Guidelines</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/neural-networks">Neural Networks</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Advanced Guidelines for ML Model Training" href="/portfolio/blog/guideline-ml"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/keras-core">Introducing Keras Core</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
 <p>Exciting times lie ahead!</p>
 <h2 class="anchor anchorWithStickyNavbar_LWe7" id="why-use-keras-core">Why Use Keras Core?<a href="#why-use-keras-core" class="hash-link" aria-label="Direct link to Why Use Keras Core?" title="Direct link to Why Use Keras Core?">​</a></h2>
 <p>But wait, why are they making Keras multi-backend again? Let’s take a quick trip down memory lane. Not too long ago, Keras had the ability to run on multiple backends like Theano, TensorFlow, CNTK, and even MXNet. However, in 2018, they decided to focus exclusively on TensorFlow as other backends discontinued development. But times have changed! Fast forward to 2023, and we see TensorFlow dominating the production ML space with a market share of 55% to 60%. On the other hand, PyTorch has captured the ML research realm with a market share of 40% to 45%. Meanwhile, JAX, although with a smaller market share, has gained recognition from leading players in generative AI. It’s clear that each framework has its strengths and user base. Keras Core enables the users to leverage the power of all three frameworks simultaneously.</p>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Misinterpretations&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/venn">Venn Diagrams: Art &amp; Pitfalls</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-visualization">Data Visualization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/venn-diagrams">Venn Diagrams</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pitfalls">Pitfalls</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/misinterpretations">Misinterpretations</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Venn Diagrams: Art &amp; Pitfalls" href="/portfolio/blog/venn"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Misinterpretations&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/venn">Venn Diagrams: Art &amp; Pitfalls</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-visualization">Data Visualization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/venn-diagrams">Venn Diagrams</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pitfalls">Pitfalls</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/misinterpretations">Misinterpretations</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Venn Diagrams: Art &amp; Pitfalls" href="/portfolio/blog/venn"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->13 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Model Training&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Understanding Model Training"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->13 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
 <p>Welcome to the captivating realm of machine learning, where algorithms breathe life into data and unveil patterns that were once hidden in the shadows. Before we dive into the intricate dance of code and data, let’s take a moment to understand the essence of model training.</p>
 <p>Imagine yourself as an artisan, crafting a masterpiece from raw materials. Just as a painter starts with a blank canvas, you begin with a dataset rich in information. This dataset is your palette, and your model is the brush that will paint the future. 🎨🤖</p>
 <p>Model training is the process of imbuing your creation with the ability to learn from data and make predictions. Just as a symphony conductor guides each musician to play in harmony, you guide your model through the data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/model-training">Model Training</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/guidelines">Guidelines</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/neural-networks">Neural Networks</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Advanced Guidelines for ML Model Training" href="/portfolio/blog/guideline-ml"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Mythology&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
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2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">Search</span></span><span class="DocSearch-Button-Keys"></span></button></div></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div></nav><div id="__docusaurus_skipToContent_fallback" class="main-wrapper mainWrapper_z2l0"><div class="container margin-vert--lg"><div class="row"><aside class="col col--3"><nav class="sidebar_re4s thin-scrollbar" aria-label="Blog recent posts navigation"><div class="sidebarItemTitle_pO2u margin-bottom--md">Recent posts</div><ul class="sidebarItemList_Yudw clean-list"><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Mythology&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Are you tired of your 9-to-5 job and looking for a quick way to become a millionaire? Do you believe that you can get rich overnight by learning a few machine learning algorithms? Well, hold on to your hats because I have some news for you: there’s no shortcut to success in data science.</p>
 <p>Let’s face it, we all want to be successful and financially stable. And with the booming field of data science and machine learning, it’s tempting to believe that we can achieve our financial dreams by simply learning a few skills and jumping on the bandwagon. Unfortunately, the reality is far from that.</p>
 <p>Here, we’ll explore seven common myths and pitfalls of get-rich-quick schemes in data science and machine learning. So, stay tuned, and let’s debunk some myths!</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/get-rich-quick">Get Rich Quick</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/mythology">Mythology</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Get-Rich-Quick Schemes are a Bad Idea" href="/portfolio/blog/get-rich-quick"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->13 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Neural Networks&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Understanding Model Training"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->13 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-model-training">Understanding Model Training<a href="#understanding-model-training" class="hash-link" aria-label="Direct link to Understanding Model Training" title="Direct link to Understanding Model Training">​</a></h2>
 <p>Welcome to the captivating realm of machine learning, where algorithms breathe life into data and unveil patterns that were once hidden in the shadows. Before we dive into the intricate dance of code and data, let’s take a moment to understand the essence of model training.</p>
 <p>Imagine yourself as an artisan, crafting a masterpiece from raw materials. Just as a painter starts with a blank canvas, you begin with a dataset rich in information. This dataset is your palette, and your model is the brush that will paint the future. 🎨🤖</p>
 <p>Model training is the process of imbuing your creation with the ability to learn from data and make predictions. Just as a symphony conductor guides each musician to play in harmony, you guide your model through the data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/machine-learning">Machine Learning</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/model-training">Model Training</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/guidelines">Guidelines</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/neural-networks">Neural Networks</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Advanced Guidelines for ML Model Training" href="/portfolio/blog/guideline-ml"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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is Parquet format so popular?</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Pandas Dataframe&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Introduction"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/parquet">Why is Parquet format so popular?</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
 <p><a href="https://parquet.apache.org/" target="_blank" rel="noopener noreferrer">Parquet</a> is a popular columnar storage format for big data processing. It’s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we’ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we’ll compare it to the popular pandas DataFrame.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/parquet">Parquet</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-storage">Data Storage</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/spark">Spark</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pandas-dataframe">Pandas Dataframe</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Why is Parquet format so popular?" href="/portfolio/blog/parquet"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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Parquet format so popular?</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Parquet&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Introduction"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/parquet">Why is Parquet format so popular?</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
 <p><a href="https://parquet.apache.org/" target="_blank" rel="noopener noreferrer">Parquet</a> is a popular columnar storage format for big data processing. It’s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we’ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we’ll compare it to the popular pandas DataFrame.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/parquet">Parquet</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-storage">Data Storage</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/spark">Spark</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pandas-dataframe">Pandas Dataframe</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Why is Parquet format so popular?" href="/portfolio/blog/parquet"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Pitfalls&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/venn">Venn Diagrams: Art &amp; Pitfalls</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-visualization">Data Visualization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/venn-diagrams">Venn Diagrams</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pitfalls">Pitfalls</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/misinterpretations">Misinterpretations</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Venn Diagrams: Art &amp; Pitfalls" href="/portfolio/blog/venn"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Pitfalls&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/venn">Venn Diagrams: Art &amp; Pitfalls</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-visualization">Data Visualization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/venn-diagrams">Venn Diagrams</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pitfalls">Pitfalls</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/misinterpretations">Misinterpretations</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Venn Diagrams: Art &amp; Pitfalls" href="/portfolio/blog/venn"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->16 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Privacy&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Bias and Fairness in AI"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->16 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
 <p>In the realm of artificial intelligence, addressing the issue of bias and ensuring fairness in algorithms is paramount. AI systems have the potential to perpetuate and even amplify societal biases if not carefully designed and monitored. Exploring this topic delves into the ethical responsibility of AI researchers and practitioners to mitigate biases and promote equity in AI applications.</p>
 <h3 class="anchor anchorWithStickyNavbar_LWe7" id="q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications">Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?<a href="#q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications" class="hash-link" aria-label="Direct link to Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?" title="Direct link to Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?">​</a></h3>
 <p>Addressing bias and ensuring fairness in AI is of paramount importance in real-world applications because these technologies are increasingly integrated into <em>decision-making</em> processes across various domains, from finance and healthcare to criminal justice. When AI models exhibit bias, they can perpetuate and even exacerbate <em>societal inequalities</em>. For example, biased algorithms in lending can lead to discrimination against marginalized groups, and biased facial recognition systems may lead to wrongful arrests. These issues not only <em>undermine trust in AI</em> but also have real-life consequences for individuals and communities. Thus, addressing bias and ensuring fairness is an ethical imperative, promoting equity, and mitigating the harmful impact of AI on vulnerable populations.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ethics">Ethics</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/bias">Bias</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/transparency">Transparency</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/privacy">Privacy</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Exploring AI Ethics: 15 Key Q&amp;As" href="/portfolio/blog/ethics"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Pseudonymization&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="The Yin and Yang of Data Privacy"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data 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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Pseudonymization&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="The Yin and Yang of Data Privacy"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data Privacy">​</a></h2>
 <p>In the digital era, data is the new oil. It fuels innovation, drives decision-making, and propels businesses towards success. But with great power comes great responsibility. As data scientists, we must ensure that the data we handle is not misused or exploited, causing harm to the individuals it represents. Enter the twin superheroes of data protection: Anonymization and Pseudonymization.</p>
 <p>Anonymization and Pseudonymization are two essential techniques in the realm of data privacy. They serve as the yin and yang, balancing the need for data utility with the imperative of privacy protection. While they may seem similar at first glance, each has its distinct characteristics and use cases.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-privacy">Data Privacy</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/anonymization">Anonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pseudonymization">Pseudonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/python">Python</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Anonymization vs. Pseudonymization" href="/portfolio/blog/anonym-pseudonym"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;PyTorch&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/keras-core">Introducing Keras Core</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;PyTorch&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/keras-core">Introducing Keras Core</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>Hey there, data enthusiasts! Get ready to witness the revolution in the world of deep learning frameworks with the arrival of Keras Core, a preview version of the future of Keras. By Fall 2023, Keras Core will evolve into Keras 3.0, bringing remarkable advancements to the table. This groundbreaking library is a complete rewrite of the Keras codebase, establishing a modular backend architecture. What does this mean for you? Well, it enables running Keras workflows on various frameworks, starting with TensorFlow, PyTorch, and JAX.</p>
 <p>Exciting times lie ahead!</p>
 <h2 class="anchor anchorWithStickyNavbar_LWe7" id="why-use-keras-core">Why Use Keras Core?<a href="#why-use-keras-core" class="hash-link" aria-label="Direct link to Why Use Keras Core?" title="Direct link to Why Use Keras Core?">​</a></h2>
 <p>But wait, why are they making Keras multi-backend again? Let’s take a quick trip down memory lane. Not too long ago, Keras had the ability to run on multiple backends like Theano, TensorFlow, CNTK, and even MXNet. However, in 2018, they decided to focus exclusively on TensorFlow as other backends discontinued development. But times have changed! Fast forward to 2023, and we see TensorFlow dominating the production ML space with a market share of 55% to 60%. On the other hand, PyTorch has captured the ML research realm with a market share of 40% to 45%. Meanwhile, JAX, although with a smaller market share, has gained recognition from leading players in generative AI. It’s clear that each framework has its strengths and user base. Keras Core enables the users to leverage the power of all three frameworks simultaneously.</p>
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href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data 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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Python&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="The Yin and Yang of Data Privacy"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->8 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-yin-and-yang-of-data-privacy">The Yin and Yang of Data Privacy<a href="#the-yin-and-yang-of-data-privacy" class="hash-link" aria-label="Direct link to The Yin and Yang of Data Privacy" title="Direct link to The Yin and Yang of Data Privacy">​</a></h2>
 <p>In the digital era, data is the new oil. It fuels innovation, drives decision-making, and propels businesses towards success. But with great power comes great responsibility. As data scientists, we must ensure that the data we handle is not misused or exploited, causing harm to the individuals it represents. Enter the twin superheroes of data protection: Anonymization and Pseudonymization.</p>
 <p>Anonymization and Pseudonymization are two essential techniques in the realm of data privacy. They serve as the yin and yang, balancing the need for data utility with the imperative of privacy protection. While they may seem similar at first glance, each has its distinct characteristics and use cases.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-privacy">Data Privacy</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/anonymization">Anonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pseudonymization">Pseudonymization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/python">Python</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Anonymization vs. Pseudonymization" href="/portfolio/blog/anonym-pseudonym"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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format so popular?</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Spark&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Introduction"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/parquet">Why is Parquet format so popular?</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
 <p><a href="https://parquet.apache.org/" target="_blank" rel="noopener noreferrer">Parquet</a> is a popular columnar storage format for big data processing. It’s widely used in the Hadoop ecosystem and provides several benefits over traditional row-based storage formats like CSV and JSON. In this article, we’ll take a closer look at why Parquet is so popular and how it can help improve the performance and efficiency of big data processing tasks. Also, we’ll compare it to the popular pandas DataFrame.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/parquet">Parquet</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-storage">Data Storage</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/spark">Spark</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pandas-dataframe">Pandas Dataframe</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Why is Parquet format so popular?" href="/portfolio/blog/parquet"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->16 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Transparency&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="Bias and Fairness in AI"><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->16 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><h2 class="anchor anchorWithStickyNavbar_LWe7" id="bias-and-fairness-in-ai">Bias and Fairness in AI<a href="#bias-and-fairness-in-ai" class="hash-link" aria-label="Direct link to Bias and Fairness in AI" title="Direct link to Bias and Fairness in AI">​</a></h2>
 <p>In the realm of artificial intelligence, addressing the issue of bias and ensuring fairness in algorithms is paramount. AI systems have the potential to perpetuate and even amplify societal biases if not carefully designed and monitored. Exploring this topic delves into the ethical responsibility of AI researchers and practitioners to mitigate biases and promote equity in AI applications.</p>
 <h3 class="anchor anchorWithStickyNavbar_LWe7" id="q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications">Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?<a href="#q1-why-is-addressing-bias-and-ensuring-fairness-in-ai-essential-especially-in-real-world-applications" class="hash-link" aria-label="Direct link to Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?" title="Direct link to Q1: Why is addressing bias and ensuring fairness in AI essential, especially in real-world applications?">​</a></h3>
 <p>Addressing bias and ensuring fairness in AI is of paramount importance in real-world applications because these technologies are increasingly integrated into <em>decision-making</em> processes across various domains, from finance and healthcare to criminal justice. When AI models exhibit bias, they can perpetuate and even exacerbate <em>societal inequalities</em>. For example, biased algorithms in lending can lead to discrimination against marginalized groups, and biased facial recognition systems may lead to wrongful arrests. These issues not only <em>undermine trust in AI</em> but also have real-life consequences for individuals and communities. Thus, addressing bias and ensuring fairness is an ethical imperative, promoting equity, and mitigating the harmful impact of AI on vulnerable populations.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ai">AI</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/ethics">Ethics</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/bias">Bias</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/transparency">Transparency</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/privacy">Privacy</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Exploring AI Ethics: 15 Key Q&amp;As" href="/portfolio/blog/ethics"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Venn Diagrams&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/venn">Venn Diagrams: Art &amp; Pitfalls</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-visualization">Data Visualization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/venn-diagrams">Venn Diagrams</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pitfalls">Pitfalls</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/misinterpretations">Misinterpretations</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Venn Diagrams: Art &amp; Pitfalls" href="/portfolio/blog/venn"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with &quot;Venn Diagrams&quot;</h1><a href="/portfolio/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/portfolio/blog/venn">Venn Diagrams: Art &amp; Pitfalls</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-science">Data Science</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/data-visualization">Data Visualization</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/venn-diagrams">Venn Diagrams</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/pitfalls">Pitfalls</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/blog/tags/misinterpretations">Misinterpretations</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Venn Diagrams: Art &amp; Pitfalls" href="/portfolio/blog/venn"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">Search</span></span><span class="DocSearch-Button-Keys"></span></button></div></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div></nav><div id="__docusaurus_skipToContent_fallback" class="main-wrapper mainWrapper_z2l0"><div class="container margin-vert--lg"><div class="row"><aside class="col col--3"><nav class="sidebar_re4s thin-scrollbar" aria-label="Blog recent posts navigation"><div class="sidebarItemTitle_pO2u margin-bottom--md">Recent posts</div><ul class="sidebarItemList_Yudw clean-list"><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><article itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."><header><h1 class="title_f1Hy" itemprop="headline">Venn Diagrams: Art &amp; Pitfalls</h1><div class="container_mt6G margin-vert--md"><time datetime="2024-04-05T14:49:00.000Z" itemprop="datePublished">April 5, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p>
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2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">Search</span></span><span class="DocSearch-Button-Keys"></span></button></div></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div></nav><div id="__docusaurus_skipToContent_fallback" class="main-wrapper mainWrapper_z2l0"><div class="container margin-vert--lg"><div class="row"><aside class="col col--3"><nav class="sidebar_re4s thin-scrollbar" aria-label="Blog recent posts navigation"><div class="sidebarItemTitle_pO2u margin-bottom--md">Recent posts</div><ul class="sidebarItemList_Yudw clean-list"><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/anonym-pseudonym">Anonymization vs. Pseudonymization</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/ethics">Exploring AI Ethics: 15 Key Q&amp;As</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/get-rich-quick">Get-Rich-Quick Schemes are a Bad Idea</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/guideline-ml">Advanced Guidelines for ML Model Training</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/portfolio/blog/keras-core">Introducing Keras Core</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><article itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data."><header><h1 class="title_f1Hy" itemprop="headline">Venn Diagrams: Art &amp; Pitfalls</h1><div class="container_mt6G margin-vert--md"><time datetime="2024-04-09T10:32:08.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><span itemprop="name">Morteza Hosseini</span></div><small class="avatar__subtitle" itemprop="description">Data scientist / ML engineer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><p>In the world of data visualization, few tools have gained as much recognition as Venn diagrams. These overlapping circles have become synonymous with illustrating set relationships, making complex data more accessible at a glance. Yet, while Venn diagrams are undeniably valuable, they also have a propensity to mislead. As a data scientist, I’ve encountered their allure and their limitations. In this article, we’ll delve into why Venn diagrams, despite their apparent simplicity, can be misleading if not used with caution. We’ll explore the intricacies that lie beneath those overlapping circles, shedding light on when and how to employ them effectively, and when to turn to alternative visualization methods for a more accurate representation of data.</p>
 <p>The followings are some reasons why Venn diagrams can sometimes be considered misleading.</p>
 <h2 class="anchor anchorWithStickyNavbar_LWe7" id="misrepresentation-of-set-sizes">Misrepresentation of Set Sizes<a href="#misrepresentation-of-set-sizes" class="hash-link" aria-label="Direct link to Misrepresentation of Set Sizes" title="Direct link to Misrepresentation of Set Sizes">​</a></h2>
 <p>Venn diagrams, often characterized by their overlapping circles, can inadvertently create a false impression of the sizes of the sets they represent. The issue arises from the inherent limitations of Venn diagrams, where the circles’ sizes are typically not proportional to the actual cardinalities of the sets. This can lead to a misperception of the data, as viewers may wrongly assume that each set is of equal size or that the proportions between sets differ from reality. Such misrepresentations can have far-reaching consequences, impacting decision-making processes and resource allocation strategies. It underscores the importance of using Venn diagrams judiciously and considering alternative visualization methods when precision in conveying set sizes is paramount in data analysis.</p>
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index 896ac992..3dee8e22 100644
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diff --git a/docs/best-practices/pep8/index.html b/docs/best-practices/pep8/index.html
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index 089055b3..edd7aa0e 100644
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diff --git a/docs/computer-vision/index.html b/docs/computer-vision/index.html
index c01dfd69..56e9417d 100644
--- a/docs/computer-vision/index.html
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7.53c-.34.3-.13.87.33.87H5v7c0 .55.45 1 1 1h3c.55 0 1-.45 1-1z" fill="currentColor"></path></svg></a></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item"><a class="breadcrumbs__link" itemprop="item" href="/portfolio/docs/data-structure/"><span itemprop="name">Data Structures</span></a><meta itemprop="position" content="1"></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">A Guide to Hashing</span><meta itemprop="position" content="2"></li></ul></nav><div class="tocCollapsible_ETCw theme-doc-toc-mobile tocMobile_ITEo"><button type="button" class="clean-btn tocCollapsibleButton_TO0P">On this page</button></div><div class="theme-doc-markdown markdown"><h1>A Guide to Hashing</h1>
+<h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction">​</a></h2>
+<p>Hashing is a fundamental concept in computer science and plays a crucial role in various applications. At its core, hashing involves mapping data of arbitrary size (such as strings, files, or even entire databases) to fixed-size values. These fixed-size values are typically numeric and are known as <strong>hash codes</strong> or <strong>hash values</strong>.</p>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="why-do-we-need-hashing">Why Do We Need Hashing?<a href="#why-do-we-need-hashing" class="hash-link" aria-label="Direct link to Why Do We Need Hashing?" title="Direct link to Why Do We Need Hashing?">​</a></h3>
+<p>Hashing provides several benefits:</p>
+<ol>
+<li>
+<p><strong>Data Integrity</strong>: Hashing ensures that data remains unchanged during transmission or storage. By comparing hash values before and after data transfer, we can verify whether the data has been altered.</p>
+</li>
+<li>
+<p><strong>Efficient Data Retrieval</strong>: Hashing allows us to create data structures like hash tables, which enable fast retrieval of values based on keys. Think of hash tables as efficient dictionaries where keys are hashed to indices.</p>
+</li>
+<li>
+<p><strong>Security and Cryptography</strong>: Hash functions are used in password storage, digital signatures, and cryptographic protocols. A good hash function should be resistant to collisions (two different inputs producing the same hash) and pre-image attacks (finding an input for a given hash).</p>
+</li>
+</ol>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="how-hashing-works">How Hashing Works<a href="#how-hashing-works" class="hash-link" aria-label="Direct link to How Hashing Works" title="Direct link to How Hashing Works">​</a></h3>
+<ol>
+<li>
+<p><strong>Hash Functions</strong>: A hash function takes an input (often called a <strong>message</strong>) and produces a fixed-size output (the hash value). The output is typically a sequence of characters (hexadecimal digits). Examples of hash functions include MD5, SHA-1, and SHA-256.</p>
+</li>
+<li>
+<p><strong>Deterministic</strong>: Hash functions are deterministic, meaning the same input will always produce the same hash value. This property is essential for consistency.</p>
+</li>
+<li>
+<p><strong>Fixed Output Size</strong>: Regardless of input size, the hash function generates a fixed-length output. For instance, SHA-256 produces a 256-bit (32-byte) hash.</p>
+</li>
+</ol>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="common-use-cases">Common Use Cases<a href="#common-use-cases" class="hash-link" aria-label="Direct link to Common Use Cases" title="Direct link to Common Use Cases">​</a></h3>
+<p>Hashing is used in various scenarios:</p>
+<ul>
+<li>
+<p><strong>Data Structures</strong>: Hash tables, bloom filters, and caches rely on hashing for efficient data storage and retrieval.</p>
+</li>
+<li>
+<p><strong>File Integrity</strong>: Hashing verifies file integrity by comparing hash values before and after file transfer.</p>
+</li>
+<li>
+<p><strong>Cryptographic Applications</strong>: Hash functions are used in digital signatures, password storage, and blockchain technology.</p>
+</li>
+</ul>
+<h2 class="anchor anchorWithStickyNavbar_LWe7" id="understanding-hash-functions">Understanding Hash Functions<a href="#understanding-hash-functions" class="hash-link" aria-label="Direct link to Understanding Hash Functions" title="Direct link to Understanding Hash Functions">​</a></h2>
+<p>In the previous section, we introduced the concept of hashing and its importance. Now, let&#x27;s delve deeper into hash functions--the heart of hashing.</p>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="what-is-a-hash-function">What Is a Hash Function?<a href="#what-is-a-hash-function" class="hash-link" aria-label="Direct link to What Is a Hash Function?" title="Direct link to What Is a Hash Function?">​</a></h3>
+<p>A hash function is a mathematical algorithm that takes an input and produces a fixed-size output. The output is typically represented as a sequence of characters (usually hexadecimal digits). Here are some key characteristics of hash functions:</p>
+<ol>
+<li>
+<p><strong>Deterministic</strong>: A hash function is deterministic, meaning that given the same input, it will always produce the same hash value. This property ensures consistency and predictability.</p>
+</li>
+<li>
+<p><strong>Fixed Output Size</strong>: Regardless of the input size, the hash function generates a fixed-length output. For instance, SHA-512 produces a 512-bit (64-byte) hash.</p>
+</li>
+<li>
+<p><strong>Avalanche Effect</strong>: A small change in the input should result in a significantly different hash value. This property prevents attackers from easily predicting hash collisions.</p>
+</li>
+</ol>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="commonly-used-hash-functions">Commonly Used Hash Functions<a href="#commonly-used-hash-functions" class="hash-link" aria-label="Direct link to Commonly Used Hash Functions" title="Direct link to Commonly Used Hash Functions">​</a></h3>
+<p>Several hash functions are widely used in practice:</p>
+<ol>
+<li>
+<p><strong>MD5 (Message Digest Algorithm 5)</strong>: Although widely used in the past, MD5 is now considered insecure due to vulnerabilities. It produces a 128-bit hash.</p>
+</li>
+<li>
+<p><strong>SHA-1 (Secure Hash Algorithm 1)</strong>: Like MD5, SHA-1 is also deprecated due to vulnerabilities. It produces a 160-bit hash.</p>
+</li>
+<li>
+<p><strong>SHA-256 (Secure Hash Algorithm 256)</strong>: Part of the SHA-2 family, SHA-256 is widely used for cryptographic purposes. It produces a 256-bit hash and is considered secure.</p>
+</li>
+</ol>
+<h2 class="anchor anchorWithStickyNavbar_LWe7" id="common-use-cases-for-hashing">Common Use Cases for Hashing<a href="#common-use-cases-for-hashing" class="hash-link" aria-label="Direct link to Common Use Cases for Hashing" title="Direct link to Common Use Cases for Hashing">​</a></h2>
+<p>Hashing finds applications in various domains, making it a versatile tool for data manipulation and security. Let&#x27;s explore some common use cases:</p>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="data-structures-and-hash-tables">Data Structures and Hash Tables<a href="#data-structures-and-hash-tables" class="hash-link" aria-label="Direct link to Data Structures and Hash Tables" title="Direct link to Data Structures and Hash Tables">​</a></h3>
+<p>Hash tables (or hash maps) are fundamental data structures that rely on hashing. They allow efficient storage and retrieval of key-value pairs. Here&#x27;s how they work:</p>
+<ul>
+<li>
+<p><strong>Hashing Keys</strong>: When you insert a value into a hash table, a hash function computes an index (often called a <strong>hash code</strong>) based on the key. This index determines the position where the value is stored.</p>
+</li>
+<li>
+<p><strong>Fast Retrieval</strong>: Hash tables provide constant-time average complexity for insertion, deletion, and retrieval. They are widely used in databases, caches, and language dictionaries.</p>
+</li>
+</ul>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="file-integrity-verification">File Integrity Verification<a href="#file-integrity-verification" class="hash-link" aria-label="Direct link to File Integrity Verification" title="Direct link to File Integrity Verification">​</a></h3>
+<p>Hashing ensures the integrity of files during transmission or storage. Here&#x27;s how it works:</p>
+<ul>
+<li>
+<p><strong>Checksums</strong>: Before transferring a file, compute its hash value (checksum) using a hash function (e.g., SHA-256). Share this checksum with the recipient.</p>
+</li>
+<li>
+<p><strong>Verification</strong>: After receiving the file, the recipient computes the hash value of the received file. If the computed hash matches the shared checksum, the file hasn&#x27;t been tampered with.</p>
+</li>
+</ul>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="cryptography-and-digital-signatures">Cryptography and Digital Signatures<a href="#cryptography-and-digital-signatures" class="hash-link" aria-label="Direct link to Cryptography and Digital Signatures" title="Direct link to Cryptography and Digital Signatures">​</a></h3>
+<p>Hash functions play a critical role in cryptography:</p>
+<ul>
+<li>
+<p><strong>Password Storage</strong>: Instead of storing plain-text passwords, systems store their hash values. When a user logs in, the system hashes the entered password and compares it with the stored hash.</p>
+</li>
+<li>
+<p><strong>Digital Signatures</strong>: Hash functions are used to create digital signatures. A signer hashes the message and encrypts the hash with their private key. Recipients verify the signature by decrypting the hash with the signer&#x27;s public key.</p>
+</li>
+</ul>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="content-deduplication">Content Deduplication<a href="#content-deduplication" class="hash-link" aria-label="Direct link to Content Deduplication" title="Direct link to Content Deduplication">​</a></h3>
+<p>Hashing helps identify duplicate content efficiently:</p>
+<ul>
+<li>
+<p><strong>Content Addressable Storage (CAS)</strong>: Systems store unique content (files, chunks, or blocks) based on their hash values. Duplicate content is automatically deduplicated.</p>
+</li>
+<li>
+<p><strong>Git Repositories</strong>: Git uses SHA-256 hashes to uniquely identify commits, files, and objects. This ensures data integrity and efficient storage.</p>
+</li>
+</ul>
+<h2 class="anchor anchorWithStickyNavbar_LWe7" id="implementing-hashing-in-python">Implementing Hashing in Python<a href="#implementing-hashing-in-python" class="hash-link" aria-label="Direct link to Implementing Hashing in Python" title="Direct link to Implementing Hashing in Python">​</a></h2>
+<p>Now that we&#x27;ve covered the theory behind hash functions, let&#x27;s roll up our sleeves and implement hashing in Python. We&#x27;ll explore how to compute hash values, create hash tables, and handle common scenarios.</p>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="hashing-strings">Hashing Strings<a href="#hashing-strings" class="hash-link" aria-label="Direct link to Hashing Strings" title="Direct link to Hashing Strings">​</a></h3>
+<p>Python provides the <code>hashlib</code> library, which offers various hash algorithms. Let&#x27;s start by hashing a simple string using SHA-256:</p>
+<div class="language-python codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#393A34;--prism-background-color:#f6f8fa"><div class="codeBlockTitle_Ktv7">Python</div><div class="codeBlockContent_biex"><pre tabindex="0" class="prism-code language-python codeBlock_bY9V thin-scrollbar" style="color:#393A34;background-color:#f6f8fa"><code class="codeBlockLines_e6Vv"><span class="token-line" style="color:#393A34"><span class="token keyword" style="color:#00009f">import</span><span class="token plain"> hashlib</span><br></span><span class="token-line" style="color:#393A34"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain"></span><span class="token keyword" style="color:#00009f">def</span><span class="token plain"> </span><span class="token function" style="color:#d73a49">compute_sha256_hash</span><span class="token punctuation" style="color:#393A34">(</span><span class="token plain">data</span><span class="token punctuation" style="color:#393A34">)</span><span class="token punctuation" style="color:#393A34">:</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">    sha256_hash </span><span class="token operator" style="color:#393A34">=</span><span class="token plain"> hashlib</span><span class="token punctuation" style="color:#393A34">.</span><span class="token plain">sha256</span><span class="token punctuation" style="color:#393A34">(</span><span class="token plain">data</span><span class="token punctuation" style="color:#393A34">.</span><span class="token plain">encode</span><span class="token punctuation" style="color:#393A34">(</span><span class="token punctuation" style="color:#393A34">)</span><span class="token punctuation" style="color:#393A34">)</span><span class="token punctuation" style="color:#393A34">.</span><span class="token plain">hexdigest</span><span class="token punctuation" style="color:#393A34">(</span><span class="token punctuation" style="color:#393A34">)</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">    </span><span class="token keyword" style="color:#00009f">return</span><span class="token plain"> sha256_hash</span><br></span><span class="token-line" style="color:#393A34"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain"></span><span class="token comment" style="color:#999988;font-style:italic"># Example usage</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">message </span><span class="token operator" style="color:#393A34">=</span><span class="token plain"> </span><span class="token string" style="color:#e3116c">&quot;Hello, world!&quot;</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">hash_value </span><span class="token operator" style="color:#393A34">=</span><span class="token plain"> compute_sha256_hash</span><span class="token punctuation" style="color:#393A34">(</span><span class="token plain">message</span><span class="token punctuation" style="color:#393A34">)</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain"></span><span class="token keyword" style="color:#00009f">print</span><span class="token punctuation" style="color:#393A34">(</span><span class="token string-interpolation string" style="color:#e3116c">f&quot;SHA-256 hash of &#x27;</span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">{</span><span class="token string-interpolation interpolation">message</span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">}</span><span class="token string-interpolation string" style="color:#e3116c">&#x27;:\n</span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">{</span><span class="token string-interpolation interpolation">hash_value</span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">}</span><span class="token string-interpolation string" style="color:#e3116c">&quot;</span><span class="token punctuation" style="color:#393A34">)</span><br></span></code></pre><div class="buttonGroup__atx"><button type="button" aria-label="Copy code to clipboard" title="Copy" class="clean-btn"><span class="copyButtonIcons_eSgA" aria-hidden="true"><svg viewBox="0 0 24 24" class="copyButtonIcon_y97N"><path fill="currentColor" d="M19,21H8V7H19M19,5H8A2,2 0 0,0 6,7V21A2,2 0 0,0 8,23H19A2,2 0 0,0 21,21V7A2,2 0 0,0 19,5M16,1H4A2,2 0 0,0 2,3V17H4V3H16V1Z"></path></svg><svg viewBox="0 0 24 24" class="copyButtonSuccessIcon_LjdS"><path fill="currentColor" d="M21,7L9,19L3.5,13.5L4.91,12.09L9,16.17L19.59,5.59L21,7Z"></path></svg></span></button></div></div></div>
+<div class="codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#393A34;--prism-background-color:#f6f8fa"><div class="codeBlockContent_biex"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#393A34;background-color:#f6f8fa"><code class="codeBlockLines_e6Vv"><span class="token-line" style="color:#393A34"><span class="token plain">SHA-256 hash of &#x27;Hello, world!&#x27;:</span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">315f5bdb76d078c43b8ac0064e4a0164612b1fce77c869345bfc94c75894edd3</span><br></span></code></pre><div class="buttonGroup__atx"><button type="button" aria-label="Copy code to clipboard" title="Copy" class="clean-btn"><span class="copyButtonIcons_eSgA" aria-hidden="true"><svg viewBox="0 0 24 24" class="copyButtonIcon_y97N"><path fill="currentColor" d="M19,21H8V7H19M19,5H8A2,2 0 0,0 6,7V21A2,2 0 0,0 8,23H19A2,2 0 0,0 21,21V7A2,2 0 0,0 19,5M16,1H4A2,2 0 0,0 2,3V17H4V3H16V1Z"></path></svg><svg viewBox="0 0 24 24" class="copyButtonSuccessIcon_LjdS"><path fill="currentColor" d="M21,7L9,19L3.5,13.5L4.91,12.09L9,16.17L19.59,5.59L21,7Z"></path></svg></span></button></div></div></div>
+<p>In this example, we define a function <code>compute_sha256_hash</code> that takes a string input, computes its SHA-256 hash, and returns the hexadecimal representation. Feel free to explore other hash algorithms like MD5 or SHA-1.</p>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="creating-a-simple-hash-table">Creating a Simple Hash Table<a href="#creating-a-simple-hash-table" class="hash-link" aria-label="Direct link to Creating a Simple Hash Table" title="Direct link to Creating a Simple Hash Table">​</a></h3>
+<p>Let&#x27;s create a basic hash table to store key-value pairs. We&#x27;ll use a dictionary in Python, which internally uses hashing for efficient lookups:</p>
+<div class="language-python codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#393A34;--prism-background-color:#f6f8fa"><div class="codeBlockTitle_Ktv7">Python</div><div class="codeBlockContent_biex"><pre tabindex="0" class="prism-code language-python codeBlock_bY9V thin-scrollbar" style="color:#393A34;background-color:#f6f8fa"><code class="codeBlockLines_e6Vv"><span class="token-line" style="color:#393A34"><span class="token keyword" style="color:#00009f">class</span><span class="token plain"> </span><span class="token class-name">HashTable</span><span class="token punctuation" style="color:#393A34">:</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">    </span><span class="token keyword" style="color:#00009f">def</span><span class="token plain"> </span><span class="token function" style="color:#d73a49">__init__</span><span class="token punctuation" style="color:#393A34">(</span><span class="token plain">self</span><span class="token punctuation" style="color:#393A34">)</span><span class="token punctuation" style="color:#393A34">:</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">        self</span><span class="token punctuation" style="color:#393A34">.</span><span class="token plain">table </span><span class="token operator" style="color:#393A34">=</span><span class="token plain"> </span><span class="token punctuation" style="color:#393A34">{</span><span class="token punctuation" style="color:#393A34">}</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">    </span><span class="token keyword" style="color:#00009f">def</span><span class="token plain"> </span><span class="token function" style="color:#d73a49">insert</span><span class="token punctuation" style="color:#393A34">(</span><span class="token plain">self</span><span class="token punctuation" style="color:#393A34">,</span><span class="token plain"> key</span><span class="token punctuation" style="color:#393A34">,</span><span class="token plain"> value</span><span class="token punctuation" style="color:#393A34">)</span><span class="token punctuation" style="color:#393A34">:</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">        self</span><span class="token punctuation" style="color:#393A34">.</span><span class="token plain">table</span><span class="token punctuation" style="color:#393A34">[</span><span class="token plain">key</span><span class="token punctuation" style="color:#393A34">]</span><span class="token plain"> </span><span class="token operator" style="color:#393A34">=</span><span class="token plain"> value</span><br></span><span class="token-line" style="color:#393A34"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">    </span><span class="token keyword" style="color:#00009f">def</span><span class="token plain"> </span><span class="token function" style="color:#d73a49">get</span><span class="token punctuation" style="color:#393A34">(</span><span class="token plain">self</span><span class="token punctuation" style="color:#393A34">,</span><span class="token plain"> key</span><span class="token punctuation" style="color:#393A34">)</span><span class="token punctuation" style="color:#393A34">:</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">        </span><span class="token keyword" style="color:#00009f">return</span><span class="token plain"> self</span><span class="token punctuation" style="color:#393A34">.</span><span class="token plain">table</span><span class="token punctuation" style="color:#393A34">.</span><span class="token plain">get</span><span class="token punctuation" style="color:#393A34">(</span><span class="token plain">key</span><span class="token punctuation" style="color:#393A34">,</span><span class="token plain"> </span><span class="token boolean" style="color:#36acaa">None</span><span class="token punctuation" style="color:#393A34">)</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain"></span><span class="token comment" style="color:#999988;font-style:italic"># Example usage</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">my_table </span><span class="token operator" style="color:#393A34">=</span><span class="token plain"> HashTable</span><span class="token punctuation" style="color:#393A34">(</span><span class="token punctuation" style="color:#393A34">)</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">my_table</span><span class="token punctuation" style="color:#393A34">.</span><span class="token plain">insert</span><span class="token punctuation" style="color:#393A34">(</span><span class="token string" style="color:#e3116c">&quot;apple&quot;</span><span class="token punctuation" style="color:#393A34">,</span><span class="token plain"> </span><span class="token number" style="color:#36acaa">42</span><span class="token punctuation" style="color:#393A34">)</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">my_table</span><span class="token punctuation" style="color:#393A34">.</span><span class="token plain">insert</span><span class="token punctuation" style="color:#393A34">(</span><span class="token string" style="color:#e3116c">&quot;banana&quot;</span><span class="token punctuation" style="color:#393A34">,</span><span class="token plain"> </span><span class="token number" style="color:#36acaa">17</span><span class="token punctuation" style="color:#393A34">)</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain"></span><span class="token keyword" style="color:#00009f">print</span><span class="token punctuation" style="color:#393A34">(</span><span class="token string-interpolation string" style="color:#e3116c">f&quot;Value for &#x27;apple&#x27;: </span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">{</span><span class="token string-interpolation interpolation">my_table</span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">.</span><span class="token string-interpolation interpolation">get</span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">(</span><span class="token string-interpolation interpolation string" style="color:#e3116c">&#x27;apple&#x27;</span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">)</span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">}</span><span class="token string-interpolation string" style="color:#e3116c">&quot;</span><span class="token punctuation" style="color:#393A34">)</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain"></span><span class="token keyword" style="color:#00009f">print</span><span class="token punctuation" style="color:#393A34">(</span><span class="token string-interpolation string" style="color:#e3116c">f&quot;Value for &#x27;banana&#x27;: </span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">{</span><span class="token string-interpolation interpolation">my_table</span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">.</span><span class="token string-interpolation interpolation">get</span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">(</span><span class="token string-interpolation interpolation string" style="color:#e3116c">&#x27;banana&#x27;</span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">)</span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">}</span><span class="token string-interpolation string" style="color:#e3116c">&quot;</span><span class="token punctuation" style="color:#393A34">)</span><br></span></code></pre><div class="buttonGroup__atx"><button type="button" aria-label="Copy code to clipboard" title="Copy" class="clean-btn"><span class="copyButtonIcons_eSgA" aria-hidden="true"><svg viewBox="0 0 24 24" class="copyButtonIcon_y97N"><path fill="currentColor" d="M19,21H8V7H19M19,5H8A2,2 0 0,0 6,7V21A2,2 0 0,0 8,23H19A2,2 0 0,0 21,21V7A2,2 0 0,0 19,5M16,1H4A2,2 0 0,0 2,3V17H4V3H16V1Z"></path></svg><svg viewBox="0 0 24 24" class="copyButtonSuccessIcon_LjdS"><path fill="currentColor" d="M21,7L9,19L3.5,13.5L4.91,12.09L9,16.17L19.59,5.59L21,7Z"></path></svg></span></button></div></div></div>
+<div class="codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#393A34;--prism-background-color:#f6f8fa"><div class="codeBlockContent_biex"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#393A34;background-color:#f6f8fa"><code class="codeBlockLines_e6Vv"><span class="token-line" style="color:#393A34"><span class="token plain">Value for &#x27;apple&#x27;: 42</span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">Value for &#x27;banana&#x27;: 17</span><br></span></code></pre><div class="buttonGroup__atx"><button type="button" aria-label="Copy code to clipboard" title="Copy" class="clean-btn"><span class="copyButtonIcons_eSgA" aria-hidden="true"><svg viewBox="0 0 24 24" class="copyButtonIcon_y97N"><path fill="currentColor" d="M19,21H8V7H19M19,5H8A2,2 0 0,0 6,7V21A2,2 0 0,0 8,23H19A2,2 0 0,0 21,21V7A2,2 0 0,0 19,5M16,1H4A2,2 0 0,0 2,3V17H4V3H16V1Z"></path></svg><svg viewBox="0 0 24 24" class="copyButtonSuccessIcon_LjdS"><path fill="currentColor" d="M21,7L9,19L3.5,13.5L4.91,12.09L9,16.17L19.59,5.59L21,7Z"></path></svg></span></button></div></div></div>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="file-integrity-checks">File Integrity Checks<a href="#file-integrity-checks" class="hash-link" aria-label="Direct link to File Integrity Checks" title="Direct link to File Integrity Checks">​</a></h3>
+<p>Hashing is commonly used to verify file integrity. Suppose you have a file, and you want to ensure it hasn&#x27;t been tampered with during transfer. Compute the hash (checksum) before sending the file and share it with the recipient. Upon receiving the file, the recipient computes the hash again and compares it with the shared checksum.</p>
+<h2 class="anchor anchorWithStickyNavbar_LWe7" id="tips-and-best-practices">Tips and Best Practices<a href="#tips-and-best-practices" class="hash-link" aria-label="Direct link to Tips and Best Practices" title="Direct link to Tips and Best Practices">​</a></h2>
+<p>In this section, we&#x27;ll explore practical tips and best practices related to hashing in Python. Whether you&#x27;re a seasoned data scientist or a curious beginner, these insights will enhance your understanding and help you write robust code.</p>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="choose-the-right-hash-function">Choose the Right Hash Function<a href="#choose-the-right-hash-function" class="hash-link" aria-label="Direct link to Choose the Right Hash Function" title="Direct link to Choose the Right Hash Function">​</a></h3>
+<p>When selecting a hash function, consider your specific use case:</p>
+<ul>
+<li>
+<p><strong>Security</strong>: If you&#x27;re dealing with sensitive data (passwords, cryptographic keys), opt for a strong hash function like SHA-256 or SHA-512. These functions are designed to resist attacks and collisions.</p>
+</li>
+<li>
+<p><strong>Speed</strong>: For non-cryptographic purposes (e.g., hash tables), choose a faster hash function like MurmurHash or CityHash. These trade security for performance.</p>
+</li>
+<li>
+<p><strong>Uniqueness</strong>: If you need unique identifiers (e.g., Git commit hashes), SHA-1 or SHA-256 are excellent choices.</p>
+</li>
+</ul>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="salting-for-passwords">Salting for Passwords<a href="#salting-for-passwords" class="hash-link" aria-label="Direct link to Salting for Passwords" title="Direct link to Salting for Passwords">​</a></h3>
+<p>When hashing passwords, always use <strong>salting</strong>. Salting involves adding a unique random value (the salt) to the password before hashing. Here&#x27;s how it works:</p>
+<div class="language-python codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#393A34;--prism-background-color:#f6f8fa"><div class="codeBlockTitle_Ktv7">Python</div><div class="codeBlockContent_biex"><pre tabindex="0" class="prism-code language-python codeBlock_bY9V thin-scrollbar" style="color:#393A34;background-color:#f6f8fa"><code class="codeBlockLines_e6Vv"><span class="token-line" style="color:#393A34"><span class="token keyword" style="color:#00009f">import</span><span class="token plain"> hashlib</span><br></span><span class="token-line" style="color:#393A34"><span class="token plain"></span><span class="token keyword" style="color:#00009f">import</span><span class="token plain"> secrets</span><br></span><span class="token-line" style="color:#393A34"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain"></span><span class="token keyword" style="color:#00009f">def</span><span class="token plain"> </span><span class="token function" style="color:#d73a49">hash_password</span><span class="token punctuation" style="color:#393A34">(</span><span class="token plain">password</span><span class="token punctuation" style="color:#393A34">)</span><span class="token punctuation" style="color:#393A34">:</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">    salt </span><span class="token operator" style="color:#393A34">=</span><span class="token plain"> secrets</span><span class="token punctuation" style="color:#393A34">.</span><span class="token plain">token_bytes</span><span class="token punctuation" style="color:#393A34">(</span><span class="token number" style="color:#36acaa">16</span><span class="token punctuation" style="color:#393A34">)</span><span class="token plain">  </span><span class="token comment" style="color:#999988;font-style:italic"># Generate a random salt</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">    salted_password </span><span class="token operator" style="color:#393A34">=</span><span class="token plain"> salt </span><span class="token operator" style="color:#393A34">+</span><span class="token plain"> password</span><span class="token punctuation" style="color:#393A34">.</span><span class="token plain">encode</span><span class="token punctuation" style="color:#393A34">(</span><span class="token punctuation" style="color:#393A34">)</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">    hashed_password </span><span class="token operator" style="color:#393A34">=</span><span class="token plain"> hashlib</span><span class="token punctuation" style="color:#393A34">.</span><span class="token plain">sha256</span><span class="token punctuation" style="color:#393A34">(</span><span class="token plain">salted_password</span><span class="token punctuation" style="color:#393A34">)</span><span class="token punctuation" style="color:#393A34">.</span><span class="token plain">hexdigest</span><span class="token punctuation" style="color:#393A34">(</span><span class="token punctuation" style="color:#393A34">)</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">    </span><span class="token keyword" style="color:#00009f">return</span><span class="token plain"> hashed_password</span><br></span><span class="token-line" style="color:#393A34"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain"></span><span class="token comment" style="color:#999988;font-style:italic"># Example usage</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">user_password </span><span class="token operator" style="color:#393A34">=</span><span class="token plain"> </span><span class="token string" style="color:#e3116c">&quot;my_secure_password&quot;</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">hashed_password </span><span class="token operator" style="color:#393A34">=</span><span class="token plain"> hash_password</span><span class="token punctuation" style="color:#393A34">(</span><span class="token plain">user_password</span><span class="token punctuation" style="color:#393A34">)</span><span class="token plain"></span><br></span><span class="token-line" style="color:#393A34"><span class="token plain"></span><span class="token keyword" style="color:#00009f">print</span><span class="token punctuation" style="color:#393A34">(</span><span class="token string-interpolation string" style="color:#e3116c">f&quot;Hashed password (with salt): </span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">{</span><span class="token string-interpolation interpolation">hashed_password</span><span class="token string-interpolation interpolation punctuation" style="color:#393A34">}</span><span class="token string-interpolation string" style="color:#e3116c">&quot;</span><span class="token punctuation" style="color:#393A34">)</span><br></span></code></pre><div class="buttonGroup__atx"><button type="button" aria-label="Copy code to clipboard" title="Copy" class="clean-btn"><span class="copyButtonIcons_eSgA" aria-hidden="true"><svg viewBox="0 0 24 24" class="copyButtonIcon_y97N"><path fill="currentColor" d="M19,21H8V7H19M19,5H8A2,2 0 0,0 6,7V21A2,2 0 0,0 8,23H19A2,2 0 0,0 21,21V7A2,2 0 0,0 19,5M16,1H4A2,2 0 0,0 2,3V17H4V3H16V1Z"></path></svg><svg viewBox="0 0 24 24" class="copyButtonSuccessIcon_LjdS"><path fill="currentColor" d="M21,7L9,19L3.5,13.5L4.91,12.09L9,16.17L19.59,5.59L21,7Z"></path></svg></span></button></div></div></div>
+<div class="codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#393A34;--prism-background-color:#f6f8fa"><div class="codeBlockContent_biex"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#393A34;background-color:#f6f8fa"><code class="codeBlockLines_e6Vv"><span class="token-line" style="color:#393A34"><span class="token plain">Hashed password (with salt):</span><br></span><span class="token-line" style="color:#393A34"><span class="token plain">d86b76196689d6f3d548daafea207bd24cb71b4e54e75836d6e1ffaf3e84da7c</span><br></span></code></pre><div class="buttonGroup__atx"><button type="button" aria-label="Copy code to clipboard" title="Copy" class="clean-btn"><span class="copyButtonIcons_eSgA" aria-hidden="true"><svg viewBox="0 0 24 24" class="copyButtonIcon_y97N"><path fill="currentColor" d="M19,21H8V7H19M19,5H8A2,2 0 0,0 6,7V21A2,2 0 0,0 8,23H19A2,2 0 0,0 21,21V7A2,2 0 0,0 19,5M16,1H4A2,2 0 0,0 2,3V17H4V3H16V1Z"></path></svg><svg viewBox="0 0 24 24" class="copyButtonSuccessIcon_LjdS"><path fill="currentColor" d="M21,7L9,19L3.5,13.5L4.91,12.09L9,16.17L19.59,5.59L21,7Z"></path></svg></span></button></div></div></div>
+<p>Salting prevents attackers from using precomputed rainbow tables to crack passwords.</p>
+<h3 class="anchor anchorWithStickyNavbar_LWe7" id="avoid-hash-collisions">Avoid Hash Collisions<a href="#avoid-hash-collisions" class="hash-link" aria-label="Direct link to Avoid Hash Collisions" title="Direct link to Avoid Hash Collisions">​</a></h3>
+<p>While hash collisions (two different inputs producing the same hash) are rare, they can occur. To minimize this risk:</p>
+<ul>
+<li>Use a strong hash function with a large output size (e.g., SHA-256).</li>
+<li>If handling large datasets, consider using a hash-based data structure like a bloom filter to efficiently check for potential collisions.</li>
+</ul>
+<h2 class="anchor anchorWithStickyNavbar_LWe7" id="faqs">FAQs<a href="#faqs" class="hash-link" aria-label="Direct link to FAQs" title="Direct link to FAQs">​</a></h2>
+<p>Let&#x27;s address some common questions related to hashing in Python:</p>
+<p><strong>Q: Can I use any string as a key for hashing?</strong></p>
+<p><strong>A:</strong> Yes, you can hash any string (or even binary data) using hash functions. However, keep in mind that the quality of your hash function matters. A good hash function should distribute keys uniformly to minimize collisions.</p>
+<p><strong>Q: How do I handle collisions in hash tables?</strong></p>
+<p><strong>A:</strong> Collisions occur when two different keys hash to the same index. To handle collisions:</p>
+<ul>
+<li>Use <strong>open addressing</strong> (probing) or <strong>separate chaining</strong> (linked lists) to resolve collisions.</li>
+<li>Resize the hash table dynamically to maintain a low load factor.</li>
+</ul>
+<p><strong>Q: Is there a perfect hash function that guarantees no collisions?</strong></p>
+<p><strong>A:</strong> Unfortunately, no. Due to the pigeonhole principle, if you have more keys than possible hash values, collisions are inevitable. However, cryptographic hash functions like SHA-256 provide a very low probability of collisions.</p>
+<p>Remember, hashing is a powerful tool, and understanding its nuances ensures effective and secure implementation. Feel free to explore further and experiment with different hash functions in your Python projects! 🚀</p></div><footer class="theme-doc-footer docusaurus-mt-lg"><div class="theme-doc-footer-tags-row row margin-bottom--sm"><div class="col"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/docs/tags/hash">Hash</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/docs/tags/data-structures">Data Structures</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/docs/tags/algorithms">Algorithms</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/docs/tags/tutorial">Tutorial</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/docs/tags/python">Python</a></li></ul></div></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/portfolio/docs/data-structure/"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Data Structures</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/portfolio/docs/data-structure/sorting-popular"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">A Guide to Sorting</div></a></nav></div></div><div class="col col--3"><div class="tableOfContents_bqdL thin-scrollbar theme-doc-toc-desktop"><ul class="table-of-contents table-of-contents__left-border"><li><a href="#introduction" class="table-of-contents__link toc-highlight">Introduction</a><ul><li><a href="#why-do-we-need-hashing" class="table-of-contents__link toc-highlight">Why Do We Need Hashing?</a></li><li><a href="#how-hashing-works" class="table-of-contents__link toc-highlight">How Hashing Works</a></li><li><a href="#common-use-cases" class="table-of-contents__link toc-highlight">Common Use Cases</a></li></ul></li><li><a href="#understanding-hash-functions" class="table-of-contents__link toc-highlight">Understanding Hash Functions</a><ul><li><a href="#what-is-a-hash-function" class="table-of-contents__link toc-highlight">What Is a Hash Function?</a></li><li><a href="#commonly-used-hash-functions" class="table-of-contents__link toc-highlight">Commonly Used Hash Functions</a></li></ul></li><li><a href="#common-use-cases-for-hashing" class="table-of-contents__link toc-highlight">Common Use Cases for Hashing</a><ul><li><a href="#data-structures-and-hash-tables" class="table-of-contents__link toc-highlight">Data Structures and Hash Tables</a></li><li><a href="#file-integrity-verification" class="table-of-contents__link toc-highlight">File Integrity Verification</a></li><li><a href="#cryptography-and-digital-signatures" class="table-of-contents__link toc-highlight">Cryptography and Digital Signatures</a></li><li><a href="#content-deduplication" class="table-of-contents__link toc-highlight">Content Deduplication</a></li></ul></li><li><a href="#implementing-hashing-in-python" class="table-of-contents__link toc-highlight">Implementing Hashing in Python</a><ul><li><a href="#hashing-strings" class="table-of-contents__link toc-highlight">Hashing Strings</a></li><li><a href="#creating-a-simple-hash-table" class="table-of-contents__link toc-highlight">Creating a Simple Hash Table</a></li><li><a href="#file-integrity-checks" class="table-of-contents__link toc-highlight">File Integrity Checks</a></li></ul></li><li><a href="#tips-and-best-practices" class="table-of-contents__link toc-highlight">Tips and Best Practices</a><ul><li><a href="#choose-the-right-hash-function" class="table-of-contents__link toc-highlight">Choose the Right Hash Function</a></li><li><a href="#salting-for-passwords" class="table-of-contents__link toc-highlight">Salting for Passwords</a></li><li><a href="#avoid-hash-collisions" class="table-of-contents__link toc-highlight">Avoid Hash Collisions</a></li></ul></li><li><a href="#faqs" class="table-of-contents__link toc-highlight">FAQs</a></li></ul></div></div></div></div></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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fill="currentColor"></path></svg></a></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item"><a class="breadcrumbs__link" itemprop="item" href="/portfolio/docs/data-structure/"><span itemprop="name">Data Structures</span></a><meta itemprop="position" content="1"></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">A Guide to Sorting</span><meta itemprop="position" content="2"></li></ul></nav><div class="tocCollapsible_ETCw theme-doc-toc-mobile tocMobile_ITEo"><button type="button" class="clean-btn tocCollapsibleButton_TO0P">On this page</button></div><div class="theme-doc-markdown markdown"><h1>A Guide to Sorting</h1>
 <h2 class="anchor anchorWithStickyNavbar_LWe7" id="1-introduction">1. Introduction<a href="#1-introduction" class="hash-link" aria-label="Direct link to 1. Introduction" title="Direct link to 1. Introduction">​</a></h2>
 <p>Sorting is like tidying up your room after a wild party. But instead of picking up empty cups and crumpled napkins, we&#x27;re wrangling data. Imagine a chaotic array of numbers, letters, or even mystical runes (hey, we don&#x27;t judge). Our mission? To transform this unruly mess into a neat, orderly sequence.</p>
 <h3 class="anchor anchorWithStickyNavbar_LWe7" id="why-sort">Why Sort?<a href="#why-sort" class="hash-link" aria-label="Direct link to Why Sort?" title="Direct link to Why Sort?">​</a></h3>
@@ -155,7 +155,7 @@ <h3 class="anchor anchorWithStickyNavbar_LWe7" id="decision-making-elixirs">Deci
 </ul>
 <h3 class="anchor anchorWithStickyNavbar_LWe7" id="parting-spell">Parting Spell<a href="#parting-spell" class="hash-link" aria-label="Direct link to Parting Spell" title="Direct link to Parting Spell">​</a></h3>
 <p>So, fellow enchanters, choose wisely. Whether you&#x27;re organizing a potion cabinet or sorting a massive dataset, remember: each algorithm has its own wand, and the right one can turn chaos into enchantment. 🌟🔮📜</p>
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+<p>Now, go forth and sort—may your lists be ordered!</p></div><footer class="theme-doc-footer docusaurus-mt-lg"><div class="theme-doc-footer-tags-row row margin-bottom--sm"><div class="col"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/docs/tags/sorting">Sorting</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/docs/tags/data-structures">Data Structures</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/docs/tags/algorithms">Algorithms</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/docs/tags/tutorial">Tutorial</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/portfolio/docs/tags/python">Python</a></li></ul></div></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/portfolio/docs/data-structure/hash"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">A Guide to Hashing</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/portfolio/docs/eda/"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Exploratory Data Analysis</div></a></nav></div></div><div class="col col--3"><div class="tableOfContents_bqdL thin-scrollbar theme-doc-toc-desktop"><ul class="table-of-contents table-of-contents__left-border"><li><a href="#1-introduction" class="table-of-contents__link toc-highlight">1. Introduction</a><ul><li><a href="#why-sort" class="table-of-contents__link toc-highlight">Why Sort?</a></li><li><a href="#code-quest-begins" class="table-of-contents__link toc-highlight">Code Quest Begins</a></li></ul></li><li><a href="#2-bubble-sort" class="table-of-contents__link toc-highlight">2. Bubble Sort</a><ul><li><a href="#pythonic-bubbles" class="table-of-contents__link toc-highlight">Pythonic Bubbles</a></li></ul></li><li><a href="#3-merge-sort" class="table-of-contents__link toc-highlight">3. Merge Sort</a><ul><li><a href="#pythonic-tea-ceremony" class="table-of-contents__link toc-highlight">Pythonic Tea Ceremony</a></li></ul></li><li><a href="#4-insertion-sort" class="table-of-contents__link toc-highlight">4. Insertion Sort</a><ul><li><a href="#pythonic-card-tricks" class="table-of-contents__link toc-highlight">Pythonic Card Tricks</a></li></ul></li><li><a href="#5-quick-sort" class="table-of-contents__link toc-highlight">5. Quick Sort</a><ul><li><a href="#pythonic-dance-moves" class="table-of-contents__link toc-highlight">Pythonic Dance Moves</a></li></ul></li><li><a href="#6-heap-sort" class="table-of-contents__link toc-highlight">6. Heap Sort</a><ul><li><a href="#pythonic-library-stacks" class="table-of-contents__link toc-highlight">Pythonic Library Stacks</a></li></ul></li><li><a href="#7-tim-sort" class="table-of-contents__link toc-highlight">7. Tim Sort</a><ul><li><a href="#pythonic-incantations" class="table-of-contents__link toc-highlight">Pythonic Incantations</a></li></ul></li><li><a href="#8-radix-sort" class="table-of-contents__link toc-highlight">8. Radix Sort</a><ul><li><a href="#pythonic-incantations-1" class="table-of-contents__link toc-highlight">Pythonic Incantations</a></li></ul></li><li><a href="#9-when-to-use-which-sorting-algorithm" class="table-of-contents__link toc-highlight">9. When to Use Which Sorting Algorithm</a><ul><li><a href="#the-sorting-compass" class="table-of-contents__link toc-highlight">The Sorting Compass</a></li><li><a href="#decision-making-elixirs" class="table-of-contents__link toc-highlight">Decision-Making Elixirs</a></li><li><a href="#parting-spell" class="table-of-contents__link toc-highlight">Parting Spell</a></li></ul></li></ul></div></div></div></div></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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index c9fbf3fb..603de3a0 100644
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diff --git a/docs/eda/polars/index.html b/docs/eda/polars/index.html
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diff --git a/docs/etl/airflow-docker/index.html b/docs/etl/airflow-docker/index.html
index 3d9a4991..3f5f8c2b 100644
--- a/docs/etl/airflow-docker/index.html
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diff --git a/docs/hypertune/optuna/index.html b/docs/hypertune/optuna/index.html
index dad9a3d2..aecc9580 100644
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@@ -11,8 +11,8 @@
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diff --git a/docs/index.html b/docs/index.html
index ae552f7e..72c9bd00 100644
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diff --git a/docs/misc/index.html b/docs/misc/index.html
index 08590081..e0cfe47c 100644
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@@ -11,8 +11,8 @@
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+<script src="/portfolio/assets/js/runtime~main.748ea0f0.js" defer="defer"></script>
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diff --git a/docs/privacy/encryption/index.html b/docs/privacy/encryption/index.html
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index 2bc2c373..fe0ccbfe 100644
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index 4c646bdd..fdab868e 100644
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@@ -11,8 +11,8 @@
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1-.45 1-1v-7h1.7c.46 0 .68-.57.33-.87L12.67 3.6c-.38-.34-.96-.34-1.34 0l-8.36 7.53c-.34.3-.13.87.33.87H5v7c0 .55.45 1 1 1h3c.55 0 1-.45 1-1z" fill="currentColor"></path></svg></a></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item"><a class="breadcrumbs__link" itemprop="item" href="/portfolio/docs/python/"><span itemprop="name">Python</span></a><meta itemprop="position" content="1"></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">Generators</span><meta itemprop="position" content="2"></li></ul></nav><div class="tocCollapsible_ETCw theme-doc-toc-mobile tocMobile_ITEo"><button type="button" class="clean-btn tocCollapsibleButton_TO0P">On this page</button></div><div class="theme-doc-markdown markdown"><h1>Generators</h1>
diff --git a/docs/python/index.html b/docs/python/index.html
index b4de655d..03e297a0 100644
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@@ -11,8 +11,8 @@
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theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/privacy/">Data Privacy</a><button aria-label="Expand sidebar category &#x27;Data Privacy&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--active" aria-expanded="true" href="/portfolio/docs/python/">Python</a><button aria-label="Collapse sidebar category &#x27;Python&#x27;" type="button" class="clean-btn menu__caret"></button></div><ul style="display:block;overflow:visible;height:auto" class="menu__list"><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-2 menu__list-item"><a class="menu__link" tabindex="0" 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menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/stats/">Statistics</a><button aria-label="Expand sidebar category &#x27;Statistics&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/synthetic-data/">Synthetic Data</a><button aria-label="Expand sidebar category &#x27;Synthetic Data&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/terminal/">Terminal</a><button aria-label="Expand sidebar category &#x27;Terminal&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/time-series/">Time Series</a><button aria-label="Expand sidebar category &#x27;Time Series&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/visualization/">Data Visualization</a><button aria-label="Expand sidebar category &#x27;Data Visualization&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/xai/">Explainable AI</a><button aria-label="Expand sidebar category &#x27;Explainable AI&#x27;" type="button" class="clean-btn menu__caret"></button></div></li></ul></nav></div></div></aside><main class="docMainContainer_TBSr"><div class="container padding-top--md padding-bottom--lg"><div class="row"><div class="col docItemCol_VOVn"><div class="docItemContainer_Djhp"><article><nav class="theme-doc-breadcrumbs breadcrumbsContainer_Z_bl" aria-label="Breadcrumbs"><ul class="breadcrumbs" itemscope="" itemtype="https://schema.org/BreadcrumbList"><li class="breadcrumbs__item"><a aria-label="Home page" class="breadcrumbs__link" href="/portfolio/"><svg viewBox="0 0 24 24" class="breadcrumbHomeIcon_YNFT"><path d="M10 19v-5h4v5c0 .55.45 1 1 1h3c.55 0 1-.45 1-1v-7h1.7c.46 0 .68-.57.33-.87L12.67 3.6c-.38-.34-.96-.34-1.34 0l-8.36 7.53c-.34.3-.13.87.33.87H5v7c0 .55.45 1 1 1h3c.55 0 1-.45 1-1z" fill="currentColor"></path></svg></a></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item"><a class="breadcrumbs__link" itemprop="item" href="/portfolio/docs/python/"><span itemprop="name">Python</span></a><meta itemprop="position" content="1"></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">Pattern matching</span><meta itemprop="position" content="2"></li></ul></nav><div class="tocCollapsible_ETCw theme-doc-toc-mobile tocMobile_ITEo"><button type="button" class="clean-btn tocCollapsibleButton_TO0P">On this page</button></div><div class="theme-doc-markdown markdown"><h1>Pattern matching</h1>
diff --git a/docs/scrape/index.html b/docs/scrape/index.html
index 86ab9e36..a738b9e4 100644
--- a/docs/scrape/index.html
+++ b/docs/scrape/index.html
@@ -11,8 +11,8 @@
 <link rel="search" type="application/opensearchdescription+xml" title="Portfolio" href="/portfolio/opensearch.xml">
 
 <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" integrity="sha384-odtC+0UGzzFL/6PNoE8rX/SPcQDXBJ+uRepguP4QkPCm2LBxH3FA3y+fKSiJ+AmM" crossorigin="anonymous"><link rel="stylesheet" href="/portfolio/assets/css/styles.5107866c.css">
-<script src="/portfolio/assets/js/runtime~main.4e331487.js" defer="defer"></script>
-<script src="/portfolio/assets/js/main.1485a207.js" defer="defer"></script>
+<script src="/portfolio/assets/js/runtime~main.748ea0f0.js" defer="defer"></script>
+<script src="/portfolio/assets/js/main.0e6fe7b8.js" defer="defer"></script>
 </head>
 <body class="navigation-with-keyboard">
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M7.05,18.36c0.39-0.39,0.39-1.03,0-1.41c-0.39-0.39-1.03-0.39-1.41,0l-1.06,1.06 c-0.39,0.39-0.39,1.03,0,1.41s1.03,0.39,1.41,0L7.05,18.36z"></path></svg><svg viewBox="0 0 24 24" width="24" height="24" class="darkToggleIcon_wfgR"><path fill="currentColor" d="M9.37,5.51C9.19,6.15,9.1,6.82,9.1,7.5c0,4.08,3.32,7.4,7.4,7.4c0.68,0,1.35-0.09,1.99-0.27C17.45,17.19,14.93,19,12,19 c-3.86,0-7-3.14-7-7C5,9.07,6.81,6.55,9.37,5.51z M12,3c-4.97,0-9,4.03-9,9s4.03,9,9,9s9-4.03,9-9c0-0.46-0.04-0.92-0.1-1.36 c-0.98,1.37-2.58,2.26-4.4,2.26c-2.98,0-5.4-2.42-5.4-5.4c0-1.81,0.89-3.42,2.26-4.4C12.92,3.04,12.46,3,12,3L12,3z"></path></svg></button></div><div class="navbarSearchContainer_Bca1"><button type="button" class="DocSearch DocSearch-Button" aria-label="Search"><span class="DocSearch-Button-Container"><svg width="20" height="20" class="DocSearch-Search-Icon" viewBox="0 0 20 20"><path d="M14.386 14.386l4.0877 4.0877-4.0877-4.0877c-2.9418 2.9419-7.7115 2.9419-10.6533 0-2.9419-2.9418-2.9419-7.7115 0-10.6533 2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">Search</span></span><span class="DocSearch-Button-Keys"></span></button></div></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div></nav><div id="__docusaurus_skipToContent_fallback" class="main-wrapper mainWrapper_z2l0"><div class="docsWrapper_hBAB"><button aria-label="Scroll back to top" class="clean-btn theme-back-to-top-button backToTopButton_sjWU" type="button"></button><div class="docRoot_UBD9"><aside class="theme-doc-sidebar-container docSidebarContainer_YfHR"><div class="sidebarViewport_aRkj"><div class="sidebar_njMd"><nav aria-label="Docs sidebar" class="menu thin-scrollbar menu_SIkG"><ul class="theme-doc-sidebar-menu menu__list"><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-1 menu__list-item"><a class="menu__link" href="/portfolio/docs/">Introduction</a></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/anomaly-detection/">Anomaly Detection</a><button aria-label="Expand sidebar category &#x27;Anomaly Detection&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/best-practices/">Best Practices</a><button aria-label="Expand sidebar category &#x27;Best Practices&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category 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category &#x27;Data Format&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/hypertune/">Hyperparameter Tuning</a><button aria-label="Expand sidebar category &#x27;Hyperparameter Tuning&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/misc/">Miscellaneous</a><button aria-label="Expand sidebar category &#x27;Miscellaneous&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/privacy/">Data Privacy</a><button aria-label="Expand sidebar category &#x27;Data Privacy&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/python/">Python</a><button aria-label="Expand sidebar category &#x27;Python&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item"><div class="menu__list-item-collapsible menu__list-item-collapsible--active"><a class="menu__link menu__link--sublist menu__link--active" aria-current="page" aria-expanded="true" href="/portfolio/docs/scrape/">Web Scraping</a><button aria-label="Collapse sidebar category &#x27;Web Scraping&#x27;" type="button" class="clean-btn menu__caret"></button></div><ul style="display:block;overflow:visible;height:auto" class="menu__list"><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-2 menu__list-item"><a class="menu__link" tabindex="0" href="/portfolio/docs/scrape/jobinventory.com">Scraping Job Listings</a></li></ul></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/stats/">Statistics</a><button aria-label="Expand sidebar category &#x27;Statistics&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/synthetic-data/">Synthetic Data</a><button aria-label="Expand sidebar category &#x27;Synthetic Data&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/terminal/">Terminal</a><button aria-label="Expand sidebar category &#x27;Terminal&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/time-series/">Time Series</a><button aria-label="Expand sidebar category &#x27;Time Series&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/visualization/">Data Visualization</a><button aria-label="Expand sidebar category &#x27;Data Visualization&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/xai/">Explainable AI</a><button aria-label="Expand sidebar category &#x27;Explainable AI&#x27;" type="button" class="clean-btn menu__caret"></button></div></li></ul></nav></div></div></aside><main class="docMainContainer_TBSr"><div class="container padding-top--md padding-bottom--lg"><div class="row"><div class="col docItemCol_VOVn"><div class="docItemContainer_Djhp"><article><nav class="theme-doc-breadcrumbs breadcrumbsContainer_Z_bl" aria-label="Breadcrumbs"><ul class="breadcrumbs" itemscope="" itemtype="https://schema.org/BreadcrumbList"><li class="breadcrumbs__item"><a aria-label="Home page" class="breadcrumbs__link" href="/portfolio/"><svg viewBox="0 0 24 24" class="breadcrumbHomeIcon_YNFT"><path d="M10 19v-5h4v5c0 .55.45 1 1 1h3c.55 0 1-.45 1-1v-7h1.7c.46 0 .68-.57.33-.87L12.67 3.6c-.38-.34-.96-.34-1.34 0l-8.36 7.53c-.34.3-.13.87.33.87H5v7c0 .55.45 1 1 1h3c.55 0 1-.45 1-1z" fill="currentColor"></path></svg></a></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">Web Scraping</span><meta itemprop="position" content="1"></li></ul></nav><div class="theme-doc-markdown markdown"><h1>Web Scraping</h1>
diff --git a/docs/scrape/jobinventory.com/index.html b/docs/scrape/jobinventory.com/index.html
index 139a4f7f..12641152 100644
--- a/docs/scrape/jobinventory.com/index.html
+++ b/docs/scrape/jobinventory.com/index.html
@@ -11,8 +11,8 @@
 <link rel="search" type="application/opensearchdescription+xml" title="Portfolio" href="/portfolio/opensearch.xml">
 
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diff --git a/docs/stats/ab-test/index.html b/docs/stats/ab-test/index.html
index e595379f..18b18f31 100644
--- a/docs/stats/ab-test/index.html
+++ b/docs/stats/ab-test/index.html
@@ -11,8 +11,8 @@
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category &#x27;Data Format&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/hypertune/">Hyperparameter Tuning</a><button aria-label="Expand sidebar category &#x27;Hyperparameter Tuning&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/misc/">Miscellaneous</a><button aria-label="Expand sidebar category &#x27;Miscellaneous&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category 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aria-expanded="false" href="/portfolio/docs/scrape/">Web Scraping</a><button aria-label="Expand sidebar category &#x27;Web Scraping&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item"><div class="menu__list-item-collapsible menu__list-item-collapsible--active"><a class="menu__link menu__link--sublist menu__link--active" aria-current="page" aria-expanded="true" href="/portfolio/docs/stats/">Statistics</a><button aria-label="Collapse sidebar category &#x27;Statistics&#x27;" type="button" class="clean-btn menu__caret"></button></div><ul style="display:block;overflow:visible;height:auto" class="menu__list"><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-2 menu__list-item"><a class="menu__link" tabindex="0" href="/portfolio/docs/stats/ab-test">A/B Test</a></li><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-2 menu__list-item"><a class="menu__link" tabindex="0" href="/portfolio/docs/stats/pvalue~0.05">p-Value ~ 0.05</a></li></ul></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/synthetic-data/">Synthetic Data</a><button aria-label="Expand sidebar category &#x27;Synthetic Data&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/terminal/">Terminal</a><button aria-label="Expand sidebar category &#x27;Terminal&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/time-series/">Time Series</a><button aria-label="Expand sidebar category &#x27;Time Series&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/visualization/">Data Visualization</a><button aria-label="Expand sidebar category &#x27;Data Visualization&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/xai/">Explainable AI</a><button aria-label="Expand sidebar category &#x27;Explainable AI&#x27;" type="button" class="clean-btn menu__caret"></button></div></li></ul></nav></div></div></aside><main class="docMainContainer_TBSr"><div class="container padding-top--md padding-bottom--lg"><div class="row"><div class="col docItemCol_VOVn"><div class="docItemContainer_Djhp"><article><nav class="theme-doc-breadcrumbs breadcrumbsContainer_Z_bl" aria-label="Breadcrumbs"><ul class="breadcrumbs" itemscope="" itemtype="https://schema.org/BreadcrumbList"><li class="breadcrumbs__item"><a aria-label="Home page" class="breadcrumbs__link" href="/portfolio/"><svg viewBox="0 0 24 24" class="breadcrumbHomeIcon_YNFT"><path d="M10 19v-5h4v5c0 .55.45 1 1 1h3c.55 0 1-.45 1-1v-7h1.7c.46 0 .68-.57.33-.87L12.67 3.6c-.38-.34-.96-.34-1.34 0l-8.36 7.53c-.34.3-.13.87.33.87H5v7c0 .55.45 1 1 1h3c.55 0 1-.45 1-1z" fill="currentColor"></path></svg></a></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">Statistics</span><meta itemprop="position" content="1"></li></ul></nav><div class="theme-doc-markdown markdown"><h1>Statistics</h1>
diff --git a/docs/stats/pvalue~0.05/index.html b/docs/stats/pvalue~0.05/index.html
index 7c3edc73..5fa54051 100644
--- a/docs/stats/pvalue~0.05/index.html
+++ b/docs/stats/pvalue~0.05/index.html
@@ -11,8 +11,8 @@
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-<script src="/portfolio/assets/js/runtime~main.4e331487.js" defer="defer"></script>
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+<script src="/portfolio/assets/js/runtime~main.748ea0f0.js" defer="defer"></script>
+<script src="/portfolio/assets/js/main.0e6fe7b8.js" defer="defer"></script>
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aria-current="page" tabindex="0" href="/portfolio/docs/stats/pvalue~0.05">p-Value ~ 0.05</a></li></ul></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/synthetic-data/">Synthetic Data</a><button aria-label="Expand sidebar category &#x27;Synthetic Data&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist" aria-expanded="false" href="/portfolio/docs/terminal/">Terminal</a><button aria-label="Expand sidebar category &#x27;Terminal&#x27;" type="button" class="clean-btn menu__caret"></button></div></li><li class="theme-doc-sidebar-item-category 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menu__link--sublist" aria-expanded="false" href="/portfolio/docs/xai/">Explainable AI</a><button aria-label="Expand sidebar category &#x27;Explainable AI&#x27;" type="button" class="clean-btn menu__caret"></button></div></li></ul></nav></div></div></aside><main class="docMainContainer_TBSr"><div class="container padding-top--md padding-bottom--lg"><div class="row"><div class="col docItemCol_VOVn"><div class="docItemContainer_Djhp"><article><nav class="theme-doc-breadcrumbs breadcrumbsContainer_Z_bl" aria-label="Breadcrumbs"><ul class="breadcrumbs" itemscope="" itemtype="https://schema.org/BreadcrumbList"><li class="breadcrumbs__item"><a aria-label="Home page" class="breadcrumbs__link" href="/portfolio/"><svg viewBox="0 0 24 24" class="breadcrumbHomeIcon_YNFT"><path d="M10 19v-5h4v5c0 .55.45 1 1 1h3c.55 0 1-.45 1-1v-7h1.7c.46 0 .68-.57.33-.87L12.67 3.6c-.38-.34-.96-.34-1.34 0l-8.36 7.53c-.34.3-.13.87.33.87H5v7c0 .55.45 1 1 1h3c.55 0 1-.45 1-1z" fill="currentColor"></path></svg></a></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item"><a class="breadcrumbs__link" itemprop="item" href="/portfolio/docs/stats/"><span itemprop="name">Statistics</span></a><meta itemprop="position" content="1"></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">p-Value ~ 0.05</span><meta itemprop="position" content="2"></li></ul></nav><div class="tocCollapsible_ETCw theme-doc-toc-mobile tocMobile_ITEo"><button type="button" class="clean-btn tocCollapsibleButton_TO0P">On this page</button></div><div class="theme-doc-markdown markdown"><h1>To Reject or Not to Reject: Navigating p-Values Near 0.05</h1>
diff --git a/docs/synthetic-data/index.html b/docs/synthetic-data/index.html
index fa2df851..48b8c2ff 100644
--- a/docs/synthetic-data/index.html
+++ b/docs/synthetic-data/index.html
@@ -11,8 +11,8 @@
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+<script src="/portfolio/assets/js/runtime~main.748ea0f0.js" defer="defer"></script>
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index a2e57a02..e9cd6187 100644
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diff --git a/docs/tags/a-b-testing/index.html b/docs/tags/a-b-testing/index.html
index d9e6267d..fd883348 100644
--- a/docs/tags/a-b-testing/index.html
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@@ -11,8 +11,8 @@
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diff --git a/docs/tags/air-flow/index.html b/docs/tags/air-flow/index.html
index b223e97a..d63c9c34 100644
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index 5e0e06bc..61de2f56 100644
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index aa4761cc..676c86b8 100644
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href="/portfolio/docs/privacy/anonymization"><h2>Anonymization</h2></a><p>Learn about data anonymization and implementing it</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/misc/auto-commit"><h2>Automatic Git Commit</h2></a><p>Automate your GitHub commits using GitHub Actions</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/eda/data-balancing"><h2>Data Balancing</h2></a><p>Learn how to balance your data with Python</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/eda/missing-data"><h2>Dealing with Missing Data</h2></a><p>Learn how to handle missing data in Python</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/eda/dirty-data"><h2>Dirty Data</h2></a><p>Learn about dirty data</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/privacy/encryption"><h2>Encryption</h2></a><p>A Beginner’s Guide to Python Encryption</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/time-series/sktime"><h2>Forecasting with sktime</h2></a><p>Learn how to use sktime to forecast time-series data</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/python/generator"><h2>Generators</h2></a><p>A Hands-On Guide to Python Generators</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/computer-vision/ants-bees-classification"><h2>Image Classification App</h2></a><p>A deep learning model for image classification</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/synthetic-data/intro"><h2>Introduction</h2></a><p>An introduction to possibilites of synthetic data</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/xai/intro"><h2>Introduction to XAI</h2></a><p>An introduction to Explainable AI</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/hypertune/kerasTuner"><h2>kerasTuner</h2></a><p>How to use KerasTuner to tune hyperparameters</p></article><article 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index f6f0c111..d53a3dee 100644
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index 91b9a3e9..eadeb405 100644
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index f971f38a..893c9559 100644
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index f12a570b..3eae85e2 100644
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index 2635e8c4..43ced694 100644
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index 5d8918d3..b662f7dd 100644
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class="padding--none"><li class="tag_Nnez"><a class="tag_zVej tagWithCount_h2kH" href="/portfolio/docs/tags/lambda-functions">lambda functions<span>1</span></a></li><li class="tag_Nnez"><a class="tag_zVej tagWithCount_h2kH" href="/portfolio/docs/tags/logistic-regression">Logistic Regression<span>1</span></a></li></ul><hr></article><article><h2 class="anchor anchorWithStickyNavbar_LWe7" id="M">M<a href="#M" class="hash-link" aria-label="Direct link to M" title="Direct link to M">​</a></h2><ul class="padding--none"><li class="tag_Nnez"><a class="tag_zVej tagWithCount_h2kH" href="/portfolio/docs/tags/machine-learning">Machine Learning<span>12</span></a></li><li class="tag_Nnez"><a class="tag_zVej tagWithCount_h2kH" href="/portfolio/docs/tags/match-case">match case<span>1</span></a></li><li class="tag_Nnez"><a class="tag_zVej tagWithCount_h2kH" href="/portfolio/docs/tags/missing-data">Missing Data<span>1</span></a></li></ul><hr></article><article><h2 class="anchor 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href="/portfolio/docs/tags/pitfalls">Pitfalls<span>1</span></a></li><li class="tag_Nnez"><a class="tag_zVej tagWithCount_h2kH" href="/portfolio/docs/tags/polars">Polars<span>1</span></a></li><li class="tag_Nnez"><a class="tag_zVej tagWithCount_h2kH" href="/portfolio/docs/tags/postgre-sql">PostgreSQL<span>1</span></a></li><li class="tag_Nnez"><a class="tag_zVej tagWithCount_h2kH" href="/portfolio/docs/tags/privacy">Privacy<span>1</span></a></li><li class="tag_Nnez"><a class="tag_zVej tagWithCount_h2kH" href="/portfolio/docs/tags/programming">Programming<span>1</span></a></li><li class="tag_Nnez"><a class="tag_zVej tagWithCount_h2kH" href="/portfolio/docs/tags/python">Python<span>16</span></a></li><li class="tag_Nnez"><a class="tag_zVej tagWithCount_h2kH" href="/portfolio/docs/tags/py-torch">PyTorch<span>1</span></a></li></ul><hr></article><article><h2 class="anchor anchorWithStickyNavbar_LWe7" id="Q">Q<a href="#Q" class="hash-link" aria-label="Direct link to Q" title="Direct link to 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index d5c47c49..1ca1e01c 100644
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2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">Search</span></span><span class="DocSearch-Button-Keys"></span></button></div></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div></nav><div id="__docusaurus_skipToContent_fallback" class="main-wrapper mainWrapper_z2l0"><div class="container margin-vert--lg"><div class="row"><main class="col col--8 col--offset-2"><header class="margin-bottom--xl"><h1>12 docs tagged with &quot;Machine Learning&quot;</h1><a href="/portfolio/docs/tags">View All Tags</a></header><section class="margin-vert--lg"><article class="margin-vert--lg"><a href="/portfolio/docs/eda/data-balancing"><h2>Data Balancing</h2></a><p>Learn how to balance your data with Python</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/eda/missing-data"><h2>Dealing with Missing Data</h2></a><p>Learn how to handle missing data in Python</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/time-series/sktime"><h2>Forecasting with sktime</h2></a><p>Learn how to use sktime to forecast time-series data</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/anomaly-detection/fraud-detection"><h2>Fraud Detection</h2></a><p>Build a credit card fraud detection model using Keras</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/computer-vision/ants-bees-classification"><h2>Image Classification App</h2></a><p>A deep learning model for image classification</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/synthetic-data/intro"><h2>Introduction</h2></a><p>An introduction to possibilites of synthetic data</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/xai/intro"><h2>Introduction to XAI</h2></a><p>An introduction to Explainable AI</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/hypertune/kerasTuner"><h2>kerasTuner</h2></a><p>How to use KerasTuner to tune hyperparameters</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/misc/thresh-log-regr"><h2>Logistic Regression Thresholds</h2></a><p>Find the optimal threshold for logistic regression</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/hypertune/optuna"><h2>Optuna</h2></a><p>Hyperparameter Optimization with Optuna</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/stats/pvalue~0.05"><h2>p-Value ~ 0.05</h2></a><p>Navigate p-values near 0.05 in hypothesis testing</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/best-practices/reproducibility"><h2>Reproducibility</h2></a><p>How to make your data science projects reproducible</p></article></section></main></div></div></div><footer class="footer footer--dark"><div class="container 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2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">Search</span></span><span class="DocSearch-Button-Keys"></span></button></div></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div></nav><div id="__docusaurus_skipToContent_fallback" class="main-wrapper mainWrapper_z2l0"><div class="container margin-vert--lg"><div class="row"><main class="col col--8 col--offset-2"><header class="margin-bottom--xl"><h1>15 docs tagged with &quot;Python&quot;</h1><a href="/portfolio/docs/tags">View All Tags</a></header><section class="margin-vert--lg"><article class="margin-vert--lg"><a href="/portfolio/docs/data-structure/sorting-popular"><h2>A Guide to Sorting</h2></a><p>Implementing popular sorting algorithms in Python</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/stats/ab-test"><h2>A/B Test</h2></a><p>Introduction and implementation of A/B testing</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/misc/auto-commit"><h2>Automatic Git Commit</h2></a><p>Automate your GitHub commits using GitHub Actions</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/eda/missing-data"><h2>Dealing with Missing Data</h2></a><p>Learn how to handle missing data in Python</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/python/generator"><h2>Generators</h2></a><p>A Hands-On Guide to Python Generators</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/python/lambda"><h2>Lambda Functions</h2></a><p>An introduction to lambda functions in Python</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/visualization/lets-plot"><h2>lets-plot</h2></a><p>Learn how to plot with lets-plot Python package</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/hypertune/optuna"><h2>Optuna</h2></a><p>Hyperparameter Optimization with Optuna</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/python/match-case"><h2>Pattern matching</h2></a><p>Pattern Matching with “match case” in Python</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/best-practices/pep8"><h2>PEP 8</h2></a><p>The official style guide for Python code</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/visualization/pitfalls"><h2>Pitfalls in Data Visualization</h2></a><p>How to avoid common pitfalls in data visualization</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/eda/polars"><h2>Polars</h2></a><p>A fast library in Python designed for large datasets</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/visualization/qrcode"><h2>QR Code</h2></a><p>Learn how to generate QR codes with python-qrcode</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/terminal/rich"><h2>Rich Terminal</h2></a><p>Learn how to use the rich Python library</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/scrape/jobinventory.com"><h2>Scraping Job Listings</h2></a><p>Scraping job listings using Python</p></article></section></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="footer__links text--center"><div class="footer__links"><span class="footer__link-item">
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2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">Search</span></span><span class="DocSearch-Button-Keys"></span></button></div></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div></nav><div id="__docusaurus_skipToContent_fallback" class="main-wrapper mainWrapper_z2l0"><div class="container margin-vert--lg"><div class="row"><main class="col col--8 col--offset-2"><header class="margin-bottom--xl"><h1>16 docs tagged with &quot;Python&quot;</h1><a href="/portfolio/docs/tags">View All Tags</a></header><section class="margin-vert--lg"><article class="margin-vert--lg"><a href="/portfolio/docs/data-structure/hash"><h2>A Guide to Hashing</h2></a><p>Introduction</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/data-structure/sorting-popular"><h2>A Guide to Sorting</h2></a><p>Implementing popular sorting algorithms in Python</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/stats/ab-test"><h2>A/B Test</h2></a><p>Introduction and implementation of A/B testing</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/misc/auto-commit"><h2>Automatic Git Commit</h2></a><p>Automate your GitHub commits using GitHub Actions</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/eda/missing-data"><h2>Dealing with Missing Data</h2></a><p>Learn how to handle missing data in Python</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/python/generator"><h2>Generators</h2></a><p>A Hands-On Guide to Python Generators</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/python/lambda"><h2>Lambda Functions</h2></a><p>An introduction to lambda functions in Python</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/visualization/lets-plot"><h2>lets-plot</h2></a><p>Learn how to plot with lets-plot Python package</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/hypertune/optuna"><h2>Optuna</h2></a><p>Hyperparameter Optimization with Optuna</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/python/match-case"><h2>Pattern matching</h2></a><p>Pattern Matching with “match case” in Python</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/best-practices/pep8"><h2>PEP 8</h2></a><p>The official style guide for Python code</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/visualization/pitfalls"><h2>Pitfalls in Data Visualization</h2></a><p>How to avoid common pitfalls in data visualization</p></article><article class="margin-vert--lg"><a href="/portfolio/docs/eda/polars"><h2>Polars</h2></a><p>A fast library in Python designed for large datasets</p></article><article class="margin-vert--lg"><a 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index bb22e504..5b80d88e 100644
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@@ -11,8 +11,8 @@
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diff --git a/docs/terminal/jq/index.html b/docs/terminal/jq/index.html
index 8c689259..fab6a97b 100644
--- a/docs/terminal/jq/index.html
+++ b/docs/terminal/jq/index.html
@@ -11,8 +11,8 @@
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+<script src="/portfolio/assets/js/runtime~main.748ea0f0.js" defer="defer"></script>
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diff --git a/docs/terminal/rich/index.html b/docs/terminal/rich/index.html
index 3977c19c..42f01059 100644
--- a/docs/terminal/rich/index.html
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@@ -11,8 +11,8 @@
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diff --git a/docs/visualization/pitfalls/index.html b/docs/visualization/pitfalls/index.html
index d64eab73..eaeca260 100644
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class="theme-doc-markdown markdown"><h1>Dodging the Common Pitfalls in Data Visualization</h1>
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diff --git a/docs/xai/index.html b/docs/xai/index.html
index dcdfcde3..708f6b6a 100644
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href="/portfolio/docs/xai/intro">Introduction to XAI</a></li></ul></li></ul></nav></div></div></aside><main class="docMainContainer_TBSr"><div class="container padding-top--md padding-bottom--lg"><div class="row"><div class="col docItemCol_VOVn"><div class="docItemContainer_Djhp"><article><nav class="theme-doc-breadcrumbs breadcrumbsContainer_Z_bl" aria-label="Breadcrumbs"><ul class="breadcrumbs" itemscope="" itemtype="https://schema.org/BreadcrumbList"><li class="breadcrumbs__item"><a aria-label="Home page" class="breadcrumbs__link" href="/portfolio/"><svg viewBox="0 0 24 24" class="breadcrumbHomeIcon_YNFT"><path d="M10 19v-5h4v5c0 .55.45 1 1 1h3c.55 0 1-.45 1-1v-7h1.7c.46 0 .68-.57.33-.87L12.67 3.6c-.38-.34-.96-.34-1.34 0l-8.36 7.53c-.34.3-.13.87.33.87H5v7c0 .55.45 1 1 1h3c.55 0 1-.45 1-1z" fill="currentColor"></path></svg></a></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span 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diff --git a/docs/xai/intro/index.html b/docs/xai/intro/index.html
index fd366a4e..767db2c9 100644
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diff --git a/search/index.html b/search/index.html
index ab2574b3..6c5885d6 100644
--- a/search/index.html
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@@ -11,8 +11,8 @@
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